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A Data-Driven Simulation Approach to Quantify the Effect of Group Counseling on System Performance of College Counseling Centers

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Title: A Data-Driven Simulation Approach to Quantify the Effect of Group Counseling on System Performance of College Counseling Centers
Language: English
Authors: Youssef Hebaish (ORCID 0000-0003-3209-2790), Sohom Chatterjee, James Deegear, Miles Rucker, Hrayer Aprahamian (ORCID 0000-0002-8750-2366), Lewis Ntaimo
Source: Journal of American College Health. 2025 73(3):1240-1254.
Availability: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 15
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Group Counseling, School Counseling, Guidance Centers, Resource Allocation, Scheduling, College Students, Counseling Services, Simulation, Patients, Program Effectiveness
Geographic Terms: Texas
DOI: 10.1080/07448481.2023.2252916
ISSN: 0744-8481
1940-3208
Abstract: Objective: To investigate the effectiveness, from a system's perspective, of offering group counseling options in college counseling centers. Methods: We achieve this through a data-driven simulation-based approach with the aim of providing administrators with a quantitative tool that informs their decision-making process. Results: Our simulation experiments reveal that offering group counseling options without resource reallocation does not have the desired positive impact on the system's performance. However, with resource reallocation, our results demonstrate that the introduction of group counseling options can significantly improve the performance of the system by as much as 40%. Conclusions: Group counseling options, coupled with proper resource reallocation strategies, are effective in reducing access time of first-time patients by as much as 40%. The effect of group counseling is highly dependent on both the number of offered groups as well as their scheduling policy. Scheduling policies have to be scrutinized in light of their resulting group waiting time and resource-utilization efficiency.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1472568
Database: ERIC
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  Value: <anid>AN0183596669;acl01mar.25;2025Mar13.02:40;v2.2.500</anid> <title id="AN0183596669-1">A data-driven simulation approach to quantify the effect of group counseling on system performance of college counseling centers </title> <p>Objective: To investigate the effectiveness, from a system's perspective, of offering group counseling options in college counseling centers. Methods: We achieve this through a data-driven simulation-based approach with the aim of providing administrators with a quantitative tool that informs their decision-making process. Results: Our simulation experiments reveal that offering group counseling options without resource reallocation does not have the desired positive impact on the system's performance. However, with resource reallocation, our results demonstrate that the introduction of group counseling options can significantly improve the performance of the system by as much as 40%. Conclusions: Group counseling options, coupled with proper resource reallocation strategies, are effective in reducing access time of first-time patients by as much as 40%. The effect of group counseling is highly dependent on both the number of offered groups as well as their scheduling policy. Scheduling policies have to be scrutinized in light of their resulting group waiting time and resource-utilization efficiency.</p> <p>Keywords: Access time; college counseling; discrete-event simulation; group counseling; mental health</p> <hd id="AN0183596669-2">Introduction and motivation</hd> <p>The prevalence of mental health disorders has seen a notable increase in recent years. According to the National Institute of Mental Health, nearly one in five American adults suffer from some form of diagnosable mental health illness.[<reflink idref="bib1" id="ref1">1</reflink>] Unfortunately, the ongoing COVID-19 pandemic has only exacerbated this worrying trend by leading to an unprecedented surge of cases nationwide.[[<reflink idref="bib2" id="ref2">2</reflink>], [<reflink idref="bib4" id="ref3">4</reflink>]] While the grasp of mental health issues spans the entire population, numerous studies have revealed how it impacts certain age groups at disproportionately concerning rates. For example, nearly one-third of all reported cases in the U.S. lie within the narrow age range of 18 to 25 years old.[<reflink idref="bib1" id="ref4">1</reflink>] Given that the vast majority of college students belong to this age bracket, it is not surprising to see that college students have been particularly prone to mental health problems. A number of recent studies confirm this observation by revealing how the pandemic has negatively impacted college students.[[<reflink idref="bib5" id="ref5">5</reflink>], [<reflink idref="bib7" id="ref6">7</reflink>]] For example, the closure of campuses and the sudden shift to a remote virtual format have resulted in increased levels of isolation, loneliness, and anxiety.[<reflink idref="bib6" id="ref7">6</reflink>] As a result, the number of college students in need of counseling has seen an exponential growth, with suicide recently becoming the second leading cause of death among people aged 18 to 25.[<reflink idref="bib8" id="ref8">8</reflink>]</p> <p>Institutions of higher education have responded to this worrying trend by establishing counseling centers that provide students with mental health services.[<reflink idref="bib9" id="ref9">9</reflink>] These Counseling and Psychological Services (CAPS) centers play a pivotal role in helping students manage their mental health problems effectively by providing personalized treatments.[<reflink idref="bib10" id="ref10">10</reflink>] However, the recent increase in demand for such services has led to unprecedented strains on CAPS centers, leaving them unable to meet demand.[[<reflink idref="bib11" id="ref11">11</reflink>], [<reflink idref="bib13" id="ref12">13</reflink>]] This, coupled with limited funding, has manifested in systems with severely long wait times. For example, even before the impact of COVID-19, the average wait time for a first triage appointment across 562 college counseling centers in the U.S. was roughly 6.1 business days.[<reflink idref="bib14" id="ref13">14</reflink>] Today, this average wait time is expected to be even higher. These unacceptably long wait times have a severe impact on students by deterring them from seeking and accessing critical mental health services. Such negative consequences call for an urgent transformation in mental health services delivery at college campuses.</p> <p>CAPS centers have recently adopted a number of changes in their service delivery. For instance, by identifying early on students that would require a level of specialization that is beyond their capabilities, some CAPS facilities are referring such students to external off-campus providers.[[<reflink idref="bib15" id="ref14">15</reflink>], [<reflink idref="bib17" id="ref15">17</reflink>]] While external referrals can help counseling centers reduce their demand load, it still requires the commitment of scarce human resources to screen and identify these students. Moreover, externally referring a considerable proportion of cases may be perceived negatively by students, which might discourage them from seeking these services in the first place. CAPS centers have also experimented with telehealth, which has recently grown rapidly due to the closure of campuses.[<reflink idref="bib18" id="ref16">18</reflink>] While Nobleza et al.[<reflink idref="bib19" id="ref17">19</reflink>] and Harwood et al.[<reflink idref="bib20" id="ref18">20</reflink>] argue that most students find telehealth convenient and almost as effective as in-person sessions, telehealth does not solve resource deficiency issues. This is the case since telehealth has no direct impact on waiting time or resource allocation because counselors still need to spend time with clients <emph>via</emph> a virtual medium instead of an in-person session.</p> <p>Another promising strategy to lessen the severity of increasing demand is the introduction of group counseling options in which students attend counseling sessions in groups.[<reflink idref="bib19" id="ref19">19</reflink>]<sups>,</sups>[<reflink idref="bib21" id="ref20">21</reflink>]<sups>,</sups>[<reflink idref="bib22" id="ref21">22</reflink>] Fawcett et al.[<reflink idref="bib21" id="ref22">21</reflink>] conduct a randomized study to compare the effectiveness of individual and group counseling sessions. Although the analysis is far from comprehensive, their results are promising as they did not observe any significant differences in the efficacy of group and individual sessions. Another study by Burlingame et al.[<reflink idref="bib23" id="ref23">23</reflink>] also demonstrates the outcome equivalence of group and individual counseling. However, they highlight the need for more research on the effectiveness of plans that incorporate both group and individual counseling. While some qualitative studies argue against the effectiveness of group counseling in resolving resource-level challenges (e.g., Weatherford[<reflink idref="bib24" id="ref24">24</reflink>]) these studies are based on a single implementation; hence, the observations and conclusions may not hold in other settings. In any case, group counseling offers a compelling option to help CAPS facilities meet their growing demand. However, the effects of group counseling on system performance are still not well-understood as existing studies are primarily based on qualitative assessments.</p> <p>In this paper, we aim to more rigorously quantify the impact of group counseling on the overall performance of college CAPS centers. We achieve this by approaching the problem <emph>via</emph> a data-driven simulation model that accurately emulates the scheduling system and operational flow of college counseling centers. Doing so allows us to quantitatively assess how the introduction of group counseling options impacts the system's performance. To measure the performance of the system, we use the average wait time for a first-time appointment, referred to as <emph>access time</emph>, as the Key Performance Indicator (KPI). Understanding how group counseling impacts system performance is critical as it will allow decision-makers to manage their operations more effectively. This is especially important since the introduction of group counseling options also brings forth unique scheduling challenges.</p> <p>This work is in close collaboration with Texas A&M University's (TAMU) CAPS. The collaboration with TAMU CAPS not only provides us with crucial domain expertise but also gives us access to valuable data that is used to ensure the simulation model accurately emulates the system. The considered approach will not only quantify the magnitude of improvement (with respect to the chosen KPI), but it can also aid decision-makers in identifying good-performing group scheduling policies. To demonstrate this, we ran a series of numerical experiments on data specific to TAMU CAPS. Our results reveal that group counseling options can significantly improve access to mental health services. Moreover, our results also show that good-performing group scheduling policies can reduce access time by over 40% while maintaining a high resource-utilization efficiency. Lastly, our experiments also identify good-performing group scheduling policies that strike a balance across competing metrics.</p> <hd id="AN0183596669-3">Main challenges and objectives</hd> <p>A major challenge when analyzing the performance of CAPS facilities is the randomness that arises from various sources, including random demand for different types of services (e.g., crisis sessions, one-on-one counseling, psychiatric treatment, etc), appointment cancelations, patient no-shows, and counselor downtime. To handle such degrees of uncertainty effectively, CAPS facilities often set up a <emph>strategic plan</emph> at the beginning of each semester. This strategic plan anticipates future demand (perhaps using historical data) to identify an appropriate distribution of workload among the counselors that (hopefully) results in a good-performing system. Establishing such a plan upfront is useful because it helps CAPS directors manage available human resources effectively while ensuring fair and equitable distribution of workload across the counselors and appropriate commitment of time across the different types of services. Although the task of determining good-performing strategic plans is a challenge in itself, a number of existing studies focus on addressing this specific problem (e.g., Adair et al.,[<reflink idref="bib25" id="ref25">25</reflink>] Kilbourne et al.,[<reflink idref="bib26" id="ref26">26</reflink>] Lauriks et al.[<reflink idref="bib27" id="ref27">27</reflink>]).</p> <p>The addition of group counseling options accentuates the challenges associated with identifying good-performing strategic plans because group sessions need to be appropriately incorporated into the plan. Doing so not only requires determining the number of group sessions to offer but also a scheduling policy to allocate these sessions throughout the semester. To better illustrate this concept, Figure 1 provides an example of two scheduling policies when two types of group sessions are provided. Here, each group type is tailored to address the particular needs of a subset of students (e.g., a group titled "anxiety" is designed for students experiencing anxiety-related problems). In the example of Figure 1, each of the two group types is made up of four sessions (shaded parts). In the first scheduling policy, both group types are provided in tandem, with all four sessions scheduled at the beginning of every month. In contrast, the second policy provides the group types in sequence with the first group type scheduled during the first half of the semester and the second during the second half. Appropriate scheduling of group types and sessions across the semester has the potential to play a critical role in determining the overall performance of the system. This is especially true within the context of college counseling as the demand often follows a predictable cyclical pattern (e.g., the demand is low before major holidays and rapidly picks up before final exams). Designing a strategic plan that takes advantage of this information has the potential to substantially improve system performance.</p> <p>Incorporating group counseling options into the strategic plan requires CAPS to compromise on other types of services it provides. Counselors are often tasked to provide a multitude of services, including first-time meetings, ongoing sessions, crisis services, workshops, and a host of other activities. Each of these services needs a certain amount of time commitment which is characterized by the strategic plan. Consequently, the introduction of a new type of group counseling service requires CAPS to reduce the time commitment of some of the other service types. This reallocation of time commitments is discussed in more detail in "Case study" section. The hope is that the overall effect of this compromise will positively impact the performance of the system.</p> <p>While providing more group sessions will clearly lessen the burden on CAPS facilities, the magnitude of the improvement and the impact of group scheduling on the system are still not well-understood. This paper aims to shed light on both aspects of the problem through the use of a data-driven simulation model. In particular, this work will address two key research questions: First, how will the number of offered groups impact the performance of the system? Will there be diminishing returns, and if so, when will these diminishing returns set in? Second, for a fixed number of offered groups, does the distribution of the groups across the semester impact the system performance? If so, what are some characteristics of good-performing scheduling policies? Answering these research questions is important as doing so can provide CAPS directors with informed insights regarding the number of groups to offer and how to schedule them. These insights are of particular relevance as they do not require the hiring of additional staff, which is often not a feasible option due to budgetary limitations. This, in turn, increases the implementability of the solutions and recommendations provided in this work.</p> <hd id="AN0183596669-4">The considered simulation-based approach</hd> <p>Given the high levels of uncertainty associated with the system, as pointed out in the previous section, we consider a simulation-based modeling approach to analyze the performance of the system. Although a host of other analytical modeling approaches are available (e.g., optimization, stochastic processes, etc), simulation models have the distinct advantage of being able to capture real-life complexities that often appear in practice. An example of such a complicating factor is the dynamic adjustment of schedules based on uncontrollable random events that occur in CAPS centers (e.g., cancelation of appointments, group sessions not adequately filling up, etc). Collectively, these factors play a pivotal role in describing the overall performance of the system and hence, should not be neglected. In an effort to maintain tractability, other modeling approaches often ignore such factors by imposing simplifying assumptions. This ultimately results in a model that poorly captures the system. In contrast, simulation models are specifically designed to handle such complexities, which was one of our primary motivations for selecting a simulation approach.</p> <p>Among the different types of simulation techniques, we focus on discrete-event simulation (DES). DES models are ideal for modeling discrete-event dynamic systems such as CAPS. Examples of discrete events within CAPS include the arrival/departure of patients, appointment cancelations, and completion of group counseling sessions.</p> <p>DES models have been used to model different healthcare settings;[[<reflink idref="bib28" id="ref28">28</reflink>], [<reflink idref="bib30" id="ref29">30</reflink>], [<reflink idref="bib32" id="ref30">32</reflink>]] however, models of CAPS centers are rarely found in the literature. Recently, Chatterjee et al.[<reflink idref="bib33" id="ref31">33</reflink>] built a detailed DES model that accurately describes the scheduling system and operational flow of CAPS centers. In their paper, they use parameters related to the different services provided by CAPS, the demand distribution for each of these services, and the flow of patients to analyze the performance of the system. While they construct a comprehensive simulation model that closely emulates the operations of counseling centers, their analysis does not consider group counseling, as their primary objective is to investigate the effect of traditional one-on-one scheduling policies on the performance of the system. Consequently, their work does not address the group counseling-related research questions posed in this paper.</p> <p>In this paper, we build upon the model in Chatterjee et al.[<reflink idref="bib33" id="ref32">33</reflink>] to include the necessary components required to model group counseling. The simulation model is implemented in Simio®, a commercial simulation software, which allows us to consider several important factors. Figure 2 depicts a high-level summary of the operational flow at CAPS when group counseling is offered. Starting from the top left corner, patients arrive at the system and reserve a first-time (triage) visit in which patients are screened and an appropriate course of action is identified. The time between a patient's request and the triage visit is the access time. Once patients arrive at CAPS they will be seen by their scheduled counselor and at the end of the session the counselor will determine whether additional treatment is required. If not, or in case of an external referral, the patient exits the system. If, on the other hand, additional treatment is required, then the patient and the counselor need to schedule future sessions (represented by the "Scheduler" in Figure 2). These future sessions can either be one-on-one individual counseling sessions or group counseling sessions. Once the date of the future session arrives, the patient will reenter the system and will either go to individual or group counseling. This process repeats until the counselor determines that no future sessions are required after which the patient exits the system. Below, we detail specific components of the simulation model pertaining to the incorporation of group counseling options.</p> <p>To build a simulation model for CAPS centers involving group scheduling, we first need to establish a mechanism that incorporates group counseling options into the strategic plan. To achieve this, we define a table that summarizes all group-related activities. Recall that a group type is a series of group sessions that are specifically tailored to address the needs of a particular set of students. In this paper, we model all groups as of the same type, which caters to any patient requiring group counseling. Table 1 provides an example of this <emph>summary table</emph> where each row of the table corresponds to a specific group. For each group, the table provides information regarding the start date, the number of sessions in the series, the frequency of the sessions, the IDs of counselors assigned to provide each group, and the capacity.</p> <p>The number of group types and their focus are strategic decisions made by CAPS directors at the beginning of each semester. This choice is based on many factors including prior experience with the specific needs of the target population. Part of the objective of this work is to shed light on the impact of this choice on the overall performance of the system.</p> <p>Once the summary table has been defined, it is utilized to update the external file that stores information regarding the strategic plan. This is achieved by first defining a new type of service (group service) through a unique service type ID. Then a preprocessing procedure is triggered to appropriately label time slots that are dedicated to providing group sessions throughout the semester. Doing this generates a strategic plan that incorporates group counseling options, which adheres to the structure of the summary table. Having updated the strategic plan, the next course of action is to add custom logic to the simulation that appropriately reroutes the paths of patients that will utilize group counseling options. Although the total number of patients that will end up utilizing group sessions is random, the capacity of each group provided in the summary table specifies the maximum number of patients that can utilize such services. Determining whether a patient is suitable for a group session is highly dependent on the specific patient characteristics, and this likelihood can be extracted by analyzing historical record data. It is important to note that as the simulation is dynamically allocating patients to group counseling sessions, the number of students in each group is stored to ensure the capacity of each group is not exceeded. Lastly, if there are no additional group session vacancies and a patient is deemed suitable for group counseling, then the simulation would proceed by assigning the student to a conventional one-on-one treatment cycle.</p> <hd id="AN0183596669-5">Case study</hd> <p>In this section, using the simulation model, we conducted a case study on TAMU CAPS to answer two questions: First, how will the number of offered groups impact the performance of the system? Second, for a fixed number of offered groups, how does the distribution of the group scheduling policy impact the system performance? To answer these questions, we ran several what-if analyses to quantify the impact of different policies on the system's performance. In particular, we first investigated the impact of the number of offered groups. Then, we further explored the significance of their scheduling policy on the KPI. This was achieved by running two main sets of experiments and measuring their effect.</p> <hd id="AN0183596669-6">Data description and input analysis</hd> <p>We used a span of 18 weeks in our experiments to simulate the Fall 2019 semester, starting on September 2, 2019 up until December 30, 2019. Although the Fall 2019 semester ended before December 30, 2019, the data revealed that a number of sessions occurred during winter break, which is why we decided to include this period in the analysis. Fall 2019 was chosen to avoid anomalies due to the pandemic. The collaboration with TAMU CAPS was crucial as it provided us with rich data that was essential in building our simulation model. In particular, three main datasets were utilized in building the model: (i) individual-session patient record data, (ii) group counseling patient record data, and (iii) counselor schedule data. Input analysis is a fundamental component in discrete-event simulation as the output primarily depends on the input data. That is, if the input parameters do not provide an accurate representation of the system's functionality, the output cannot be expected to reliably predict the system's performance. Hence, the main objective of input analysis is to obtain theoretical distributions from the data that accurately capture the behavior of the different system components. In this paper, we primarily focus on the input analysis pertaining to the implementation of group counseling options.</p> <p>Although the group counseling record dataset is the main pillar in incorporating group counseling into the simulation model, the individual-session patient record dataset is also of importance. It involves the individual-session patient record data and contains information about every one-on-one counseling session provided by TAMU CAPS to all patients. Patients' identities are concealed and keyed by a unique ID to ensure anonymity. Each row in this dataset corresponds to a unique individual session, which contains patient ID, session type, events of cancelation or no-show (if any), counselor ID, and the session date and time. It is important to note that we used empirical distributions for key input parameters of our DES model, such as the number of sessions and the weekly no-show/cancelation and associated exit rates. These distributions were constructed using TAMU CAPS historical data.</p> <p>In the simulation, we assign each student a random number of sessions using an empirical distribution, shown in Figure 3, based on data from the patient-level record dataset. Sometimes a patient is unable to make it to their scheduled session, i.e., a no-show. Based on TAMU CAPS historical data, the rate of no-shows and cancelations varies weekly. This component is modeled into the simulation by assigning each student no-show and cancelation probabilities, which are obtained using historical data. We also found from the data that several students do not continue their scheduled sessions with CAPS after a no-show or a cancelation. To account for this, there are two additional probabilities assigned to students in the simulation, namely the probability of a no-show exit and the probability of a cancelation exit. These probabilities are computed from the data by dividing the total number of no-show and cancelation sessions in each week that resulted in a patient's exit by the total number of no-show and cancelation sessions for that week.</p> <p>Our decision to directly use the empirical distributions is largely driven by the fact that we want to precisely reproduce the current behavior of TAMU CAPS. Although employing empirical distributions may have the drawback of potentially overlooking rare tail events, this issue is not present in our case, as each distribution is discrete and possesses a constrained support.</p> <p>While we opt to use empirical distributions for key input parameters in our DES model, we model the arrival process using a nonstationary Poisson process. Specifically, the mean arrival rate changes on a weekly basis and is denoted by</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>λ</mi><mo stretchy="false">(</mo><mi>t</mi><mo stretchy="false">)</mo><mtext>, where </mtext><mi>t</mi><mo>∈</mo><mo>{</mo><mn>1</mn><mo>,</mo><mo>⋯</mo><mo>,</mo><mn>18</mn><mo>}</mo></mrow></math> </ephtml> . The values of these parameters are obtained by analyzing the data provided by TAMU CAPS, where</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>λ</mi><mo stretchy="false">(</mo><mi>t</mi><mo stretchy="false">)</mo></mrow></math> </ephtml> is the number of arrivals at week <emph>t</emph>. Based on these parameters, we use a set of exponential distributions (one for each week) to model the interarrival times. One key property of the exponential distribution is that it is memoryless, meaning that the probability of an event occurring in a fixed time interval does not depend on how much time has elapsed since the last event occurred. This property is particularly useful for modeling systems where the interarrival times between events are independent of each other. For example, in a queueing system, the time between customer arrivals may be independent of the time since the previous customer arrived. Another advantage of the exponential distribution is that it is mathematically and computationally easy to work with.</p> <p>Another important input parameter that was obtained from the individual-session dataset is the distribution of the time between sessions. The data revealed that the most common values for the time between sessions are 7, 14, and 21 days, which was expected since counselors tend to book follow-up sessions based on weekly increments. Chatterjee et al. provide a comprehensive discussion on model input analysis.[<reflink idref="bib33" id="ref33">33</reflink>]</p> <p>Besides the individual-session dataset, the main dataset used in the input analysis is the second dataset which contains information about group counseling sessions. Each row in this data represents a unique group counseling session, containing patient ID, group name, provider(s), cancelation (if any), and session date and time. One of the preliminary analyses that is a pillar of our model is the distribution of students with respect to counseling options. A descriptive analysis of the dataset was conducted by matching patient IDs from the individual-session and group counseling patient record datasets. The analysis revealed that 79% of all patients were assigned to a conventional one-on-one treatment cycle while the remaining 21% of patients were assigned to group counseling options. In addition, the analysis revealed that 94% of patients who were assigned to group counseling options were only assigned to a single group. Given this, in our experiments, we assumed that patients are assigned to a single group.</p> <p>Upon determining whether patients are going to utilize group counseling options, a few additional parameters become essential to estimate. First, we need to determine the point of time at which patients are triggered into a group. Currently, TAMU CAPS recommends groups to students during a preliminary individual triage session. After consulting with TAMU CAPS, it was decided that the trigger point would occur directly after this first triage session. That is, after the triage session, students that are assigned to group counseling will no longer utilize future one-on-one individual counseling. Although the current practice does not exactly adhere to this procedure (as confirmed by an analysis of the data), this assumption was imposed due to two reasons: First, a discussion with TAMU CAPS highlighted the fact that the primary objective of group counseling options is to eliminate, at least for a subset of patients, individual one-on-one counseling. Second, enforcing the aforementioned procedure maximizes the benefits of introducing group counseling options. Consequently, the resulting analysis can shed more light on the benefits of group counseling options. In other words, if (even under an optimistic setting) the benefits of group counseling are marginal, then one can reach a definitive conclusion about the effectiveness of group counseling.</p> <p>In addition to the patient-level parameters, there are also parameters related to the nature of groups that need to be considered, such as the span of groups and their size. By analyzing the group counseling record data, for each group, we set the number of sessions to 5 (which represents the average number of group sessions in TAMU CAPS), the number of assigned counselors to 2, and the frequency (per week) to 1. In addition, we assumed that groups do not get canceled due to low enrollment, which was asserted by TAMU CAPS, given the historical rarity of canceling groups. Lastly, each group was assigned a maximum capacity of 10. At this point, it is worth noting that the considered model has the capability of modeling more complex scenarios (e.g., dynamic cancelation of groups due to minimum enrollment, different number of sessions across groups, etc). However, for the purpose of this study, we opted to simplify the analysis by standardizing the structure of groups. This standardization procedure does not impact the main findings of this paper. In terms of the strategic plan, an analysis of the counselor schedule data–which contains information about the allocation of different services across the Fall 2019 semester–revealed the proportion of time committed to different services. Generally speaking, services are divided into two main types: Counseling-related and administrative services. Counseling-related services include first-time triage sessions, ongoing visits, and group treatments. In addition to these services, counselors also have administrative duties such as intern supervision and documentation. In this case study, all noncounseling related activities were designated as "other." Consequently, four types of services were considered: (i) first-time, (ii) ongoing, (iii) groups, and (iv) other. The proportion of time allocated for first-time, ongoing, group counseling, and other service types equal 8%, 25%, 0.7%, and 66.3%, respectively. These proportions were used in order to construct a specific strategic plan realization.</p> <hd id="AN0183596669-7">Simulation experiments</hd> <p>After obtaining all relevant input parameters and ensuring that the simulation model is verified and validated (see "Model validation and verification" section in Appendix), we ran simulation experiments to answer the question of how group counseling affects the system's performance. We conduct three sets of experiments: (i) We first assess the effect of group-counseling on the system's performance, (ii) Then, we explore the effect of reallocating resources that are freed up by group-counseling to other services, and (iii) We investigate the effect of allocating time slots to other services instead of group-counseling. In all experiments, the simulation first generates a strategic plan based on the proportions dedicated to each of the four service types. To provide a more comprehensive analysis, we ran the simulation for 32 replications, each with its own strategic plan realization. We estimate the required number of replications by taking into account a 95% confidence interval and a margin of error of 4 h (half a day, considering 8-h workdays) for the access time. This estimation resulted in a minimum requirement of 21 replications. However, we have opted to conduct 32 replications due to our use of a machine that has 16 processors capable of running 16 simulations concurrently. In addition to the number of groups, we also need to specify a group scheduling policy. This is governed by the start date of each of the offered groups (see Table 1). In this study, we explored a host of group scheduling policies which are as follows:</p> <p></p> <ulist> <item> A uniform policy in which groups' start dates are uniformly distributed across the semester.</item> <p></p> <item> A 50-50 policy in which half of the groups are provided at the beginning of the semester and the remaining half toward the end of the semester.</item> <p></p> </ulist> <p>• An</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><msup><mrow><mi>i</mi></mrow><mrow><mtext>th</mtext></mrow></msup></mrow></mrow></math> </ephtml> week policy in which all groups are provided in tandem starting from week <emph>i</emph>.</p> <p>In the final policy, we explore values of <emph>i</emph> ranging from 2 to 11, inclusive, with options for <emph>i</emph> being 2, 4, 6, 8, 10, and 11. For instance, a fourth week policy with eight groups implies that there are eight groups offered, and they all start at the beginning of the fourth week. While the set of considered policies is not exhaustive, they represent simple policies that CAPS centers can implement.</p> <hd id="AN0183596669-8">Assessing the effect of group-counseling</hd> <p>In the first set of experiments, we compared the performance of the aforementioned policies to a base case with no group counseling. Figure 4 provides the access time, in the form of box plots, across the different group scheduling policies when the number of offered groups is equal to 19. Note that the output of the simulation is a random variable in itself, as one simulation run generates a single sequence of possible random realizations. As such, each of the 32 replications provides an output. Each of the box plots in Figure 4 reports the maximum of these values, the minimum, the mean, the median, and the 95% confidence interval. Interestingly, the results reveal that the incorporation of group counseling options does not have a statistically significant impact on access time. This can be seen by the large overlap of the confidence intervals between the case in which no groups were offered and the remaining scenarios. In addition, we performed <emph>t</emph>-tests on the results shown in Figure 4 to determine if there is a statistically significant difference in the average access time of different policies. We performed a total of 36 pairwise <emph>t</emph>-tests. The results of these tests, which are shown in "Access time without resource reallocation" section in Appendix, indicate that there is no statistical difference between them (<emph>p</emph> value >.05). While surprising at first, this result can be intuitively explained: The key factor that impacts the access time is the proportion of the strategic plan committed to first-time slots. This proportion was preserved in the experiment, which explains why minimal differences in the access time are observed. Such a finding is important, as simply introducing group counseling options without careful thought may not lead to the desired system improvement. This might explain why some existing studies (e.g., Weatherford[<reflink idref="bib24" id="ref34">24</reflink>]) conclude that group counseling does not seem to positively impact the performance of the system.</p> <p>The above analysis indicates that, without additional considerations, the introduction of group counseling does not positively impact the access time. However, it is important to note that the introduction of group counseling reduces the demand for one-on-one individual ongoing sessions, as the subset of patients assigned to group counseling will no longer utilize this service. To better observe this effect, a series of simulation experiments were conducted in order to quantify the additional vacant ongoing slots (over the case in which no groups were offered) due to the introduction of groups. To provide a comprehensive analysis, we ran this experiment for a host of offered group numbers ranging from 4 to 27. Figure 5 plots as a function of the number of groups the percentage surplus, over the case of offering no groups, in available ongoing slots due to the introduction of group counseling. This was done for all considered group scheduling policies. Figure 5 reveals that there is a clear surplus that steadily increases with the number of offered groups. Observe that the second week policy reports the lowest surplus, which can be explained by the fact that starting all groups early in the semester cycle will not allow patients that come later in the semester to take advantage of these group counseling services. In contrast, the 11th week policy has the highest surplus as all patients have the opportunity to access group sessions. All other policies, including the uniform and 50-50 policies, report a surplus between these two extremes. To examine the difference between the policies with regard to the surplus, we conducted a series of <emph>t</emph>-tests to test the null hypothesis that there is no difference in average surplus across each pair of policies. "Surplus" section in Appendix shows the results for the aforementioned tests, which indicate that, while some policies are statistically different from others, there is sufficient evidence to conclude that some policies are the same.</p> <p>The aforementioned surplus is important, as the freed-up slots can be committed to another service type. Given this, we performed the next set of experiments, in which we increased the proportion dedicated to first-time slots by an amount equal to the observed surplus.</p> <hd id="AN0183596669-9">The effect of reallocating resources</hd> <p>In this set of experiments, we investigate the effect of increasing the proportion allocated to first-time service by the value of the surplus of each policy. For example, under a 50-50 scheduling policy, a surplus of 1.5% is observed when 16 groups are offered. In this case, the proportion dedicated to first-time slots was increased from a base value of 8% to 9.5%. Once increased, the simulation was executed again to quantify the resulting impact on access time. Figure 6 shows the access time as a function of the number of offered groups across the different group scheduling policies. The figure reveals a number of interesting observations. As the number of offered groups reduces, their access times all converge to the case when no groups are offered. As the number of offered groups increases, the access time steadily decreases under all policies. However, observe that the reduction portrays diminishing returns and eventually plateaus. This indicates that after a certain point (in our case around 16 groups), offering additional groups provides marginal benefits and hence may not be worth it. All remaining scheduling policies report reductions between these two values. In addition, by conducting a series of <emph>t</emph>-tests, we test the null hypothesis that there is no difference in average access time between any two pairs of policies. This analysis is outlined in detail in "Access time with resource reallocation" section in Appendix. The results of the <emph>t</emph>-tests show that there is not enough evidence to reject the null hypothesis of the comparison between the 10th week and the 11th week policies when the number of offered groups is 19. Similarly, since the <emph>p</emph> value of the <emph>t</emph>-test between the uniform and the fourth week policies is greater than.05, we conclude that there is not enough evidence to reject the null hypothesis. On the other hand, the <emph>p</emph> value of the test between the second week and the sixth week policies is less than.0001, which indicates that there is enough evidence to reject the null hypothesis. Based on this analysis, one might conclude that the 10th week or 11th week policies are ideal choices. However, as we discuss below, the 10th week and 11th week policies portray undesirable traits which may deter practitioners from adopting such a policy.</p> <p>A major drawback of the 11th week policy, which should not be neglected, is that it might require patients to wait for a substantial amount of time before they can start their group counseling sessions. For example, if a patient arrives at CAPS during the second week and is deemed suitable for group counseling, then under the 11th week policy the student must wait nine weeks for the group treatment to start. This long wait time to join a group is undesirable as it might further exacerbate students' mental health. Therefore, in order to make an informed recommendation, one must account for this <emph>group waiting time</emph>. To this end, Figure 7 plots the average group waiting time as a function of the number of groups under each of the considered group scheduling policies. The figure reveals that the group waiting time is heavily impacted by the scheduling policy. Among the</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><msup><mrow><mi>i</mi></mrow><mrow><mtext>th</mtext></mrow></msup></mrow></mrow></math> </ephtml> week policies, for instance, the group waiting time consistently increases as the group start dates are pushed further into the semester. The uniform policy reports the second-lowest group waiting time, while the 50-50 policy lies roughly in the middle of the</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><msup><mrow><mi>i</mi></mrow><mrow><mtext>th</mtext></mrow></msup></mrow></mrow></math> </ephtml> week policies. In addition to the variations arising from different policies, one can also observe variations in the group waiting time across the number of offered groups. In particular, as the number of offered groups increases, the group waiting time reduces. However, the reduction is much more drastic for group scheduling policies with a start date later in the semester. We have also conducted a series of <emph>t</emph>-tests, similar to the case of surplus and access time, to compare each pair of policies with regard to group waiting time. The null hypothesis of the test is that the difference between the resulting average group waiting times is zero. According to the results, we conclude that there is sufficient evidence to reject the null hypothesis, indicating that there are statistical differences between each pair.</p> <p>The conflicting results represent a tradeoff between group waiting time and access time. That is, policies that provide the most reduction in access time tend to have the highest group waiting time. Yet another important consideration that needs to be considered is the access to groups (i.e., the proportion of students that have the option of joining a group). For example, although the second week policy results in low group waiting times, it might lead to low group utilization as a large number of patients will enter the system after the group sessions have been completed. Consequently, these students will not have the option of joining groups (even if they were deemed suitable for group counseling). To quantify this, Figure 8 depicts the average utilization of groups, as a function of the number of offered groups, under each scheduling policy. Utilization is nearly 100% when the number of offered groups is low and the start date is late. For example, the utilization of the 11th week policy is 100% up until the number of offered groups exceeds 16. Observe that for policies with earlier starting dates, utilization is decreasing much faster because groups are being offered before patients get the chance to join them (which is why they are not being filled). Also, the utilization reports a sharp decrease for all policies when the number of offered groups exceeds 16. Out of all policies, the second week policy reports the lowest enrollment, which is an undesirable trait. This represents yet another tradeoff that has to be considered. Similar to group waiting time, the results of a series of <emph>t</emph>-tests to examine the null hypothesis that there is not a statistically significant difference between policies' resulting average group utilization show that there is sufficient evidence to reject that null hypothesis. In other words, when the number of offered groups is 19, the resulting average group utilization of each policy is statistically different from the others. These results are discussed in "Group waiting time and utilization" section Appendix and shown in Table 7.</p> <p>We have three seemingly conflicting metrics: (i) access time, (ii) group waiting time, and (iii) average utilization of groups. As a result, it might be difficult for decision-makers to weigh the tradeoffs across these metrics to identify an appropriate decision. We overcame this issue by providing an approach that aids decision-makers in determining good-performing strategies that satisfy certain requirements. We achieved this by narrowing down the set of feasible solutions by imposing cutoffs on certain metrics. More precisely, we first imposed a cutoff on the group waiting time. For example, after discussing with TAMU CAPS, a suitable group waiting time was identified as 20 days. Consequently, all solutions with group waiting times above 20 days were excluded from the study. This resulted in a set of 32 feasible solutions, which are displayed in Table 2. Next, to further narrow down the viable policies, we imposed a cutoff on the average group utilization. In this study, an average group utilization of 70% was selected as a cutoff value, implying that any solution with an average group utilization lower than this cutoff was eliminated from the set of feasible solutions. Out of the 32 solutions displayed in Table 2, this procedure further eliminated 25 options, which are all italicized. Lastly, out of the remaining 7 options, the solution with the lowest access time (highlighted in boldface in Table 2) was selected. Doing so leads to option 28 that offers 16 groups using the eighth week group scheduling policy. This solution has an acceptable group waiting time of 19 days, a desirable utilization of 77%, yet provides a 44% reduction in access time over the case where no groups were offered. It is important to note that the aforementioned procedure serves as an example of a possible approach to identifying a good-performing solution. Additional components can be considered. For example, if CAPS wants to avoid offering more than a certain number of groups, this information can be embedded into the procedure in order to eliminate all solutions violating the desired condition.</p> <hd id="AN0183596669-10">Investigating an alternative approach</hd> <p>In the last set of experiments, we attempted to answer a fundamental question: What if the slots that had been allocated for group counseling were directly committed to seeing first-time patients? Answering this question is important, as it might not be worthwhile to go through the hassle of introducing and setting up group counseling options. Instead, the proportion of the strategic plan that would have been dedicated to group counseling would directly be invested in increasing the proportion of first-time slots. While this <emph>alternative</emph> approach is guaranteed to have a positive impact by reducing the access time, it is unclear how it compares to scenarios with group counseling options. In order to quantitatively assess this comparison, we conducted an experiment in which we reallocated the slots that would have been used for groups to the proportion of first-time slots. Figure 9 shows the access time, as a function of the number of offered groups, of the alternative procedure and all group scheduling policies. It is apparent that, while the alternative approach indeed reduces the access time, scenarios with group counseling options consistently outperform it. In fact, even the worst-performing group scheduling policy provides a greater reduction in access time. In order to make a statistical inference about whether there is a significant difference in access time resulting from the alternative approach and group-offering policies, we conducted a series of <emph>t</emph>-tests to compare the alternative approach to different policies. The results of the <emph>t</emph>-tests, as shown in "Alternative approach" section (Appendix), suggest that there is a statically significant difference in the access time between the alternative approach, assuming 27 groups, and all other policies offering at least four groups. In other words, the average access time of all policies offering four groups is significantly lower than that of the alternative approach when the assumed number of groups is 27. In addition, the results show that there is no statistically significant difference in the average access time when the assumed number of groups is 12 and 27 under the alternative approach. This result is aligned with the premise of group counseling options, which is seeing multiple patients at the same time. That is, for a given number of offered groups, the number of forsaken ongoing appointments by the enrolled students is greater than the number of sessions of that group. In other words, the surplus is greater than the number of sessions that groups occupy. Comparing the alternative approach to the "best" policy identified earlier (i.e., eighth week with 16 offered groups), we observe that the recommended strategy reduces the access time over the alternative approach by about 30%. Such results further demonstrate the system benefits of considering group counseling options.</p> <hd id="AN0183596669-11">Conclusions and future research directions</hd> <p>In this paper, we build a discrete-event simulation model that accurately describes the operational flow of CAPS centers while considering group counseling options. The model builds upon prior work to quantitatively answer fundamental research questions related to the effectiveness, from an operational perspective, of group counseling options. In particular, using the simulation model, we investigate how, and to what extent, the number of groups offered by CAPS and their corresponding scheduling policies affect access time. Using real data from TAMU CAPS, we ran a series of simulation experiments to answer these questions. Our first set of results indicates that, without additional considerations, simply introducing group counseling options may not have the desired positive effects on the system. In fact, it may have an adverse negative effect by increasing the load on the system. This observation is in line with previous qualitative studies that report similar findings. However, by considering an appropriate reallocation of saved ongoing sessions, we ran a second set of experiments that better highlight the potential reduction in access time due to the introduction of group counseling options. Specifically, the results reveal that introducing group counseling can reduce access time by up to 50%.</p> <p>Our analysis also considers other important factors such as group waiting time and group utilization. We used the proposed simulation framework to construct a procedure that identifies good-performing solutions that strike the ideal balance between these competing metrics. The resulting solution offers 16 groups and follows the eighth week scheduling policy. The identified solution provides an acceptable group waiting time and average utilization while still improving access time by as much as 40% over the case where no groups were offered. Lastly, our experiments also reveal that simply increasing the time committed to first-time slots (instead of introducing group counseling) provides a lower reduction in the access time when compared to scenarios with group counseling. Such results highlight the benefits of considering quantitative data-driven approaches to help decision-makers identify policies that improve the performance of complex systems such as CAPS centers.</p> <p>This work can be expanded in several interesting directions. For instance, a more comprehensive search that spans a wider scope of group scheduling policies can potentially identify even better solutions. Alternatively, one can construct a more rigorous simulation-optimization procedure that facilitates the process of identifying such policies. In the context of group counseling, one interesting direction is to consider various types of groups. Incoming students have different needs, and therefore, will be recommended to join groups that are tailored to their specific needs. Referring students to group counseling is, therefore, dependent on the sequence in which different types of groups are offered. For instance, for policies where groups are offered uniformly or based on a 50-50 split, the sequence of different types of groups will impact the policy. In such a setting, it would be interesting to investigate the effect of sequencing different group types on the system's performance. Another important direction is to factor in other important KPIs beyond the access time, such as the access time for crisis patients (i.e., patients seeking emergency service and that need urgent care) and the wait time until the next service. Lastly, another important extension is to study how specific groups affect the system.</p> <hd id="AN0183596669-12">Ethics statement</hd> <p>This work was conducted in accordance with the ethical principles outlined by the Human Research Program (HRPP). An Institutional Review Board (IRB) application of determination was submitted to the HRPP, which determined that this work does not constitute research involving human resources as defined by Department of Health and Human Services and Federal Drug Administration regulations. All data used in this work is anonymized to protect personal identities. We uphold the principles of confidentiality, privacy, and data protection throughout the research process. The IRB reference number is IRB2023-0399.</p> <hd id="AN0183596669-13">Conflict of interest disclosure</hd> <p>The authors have no conflicts of interest to report. The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements, of the United States of America and received approval from the Texas A&M University Human Research Protection Program (HRPP).</p> <hd id="AN0183596669-14">Appendix</hd> <p></p> <hd id="AN0183596669-15">Model validation and verification</hd> <p>It is vital that simulation models are verified and validated to be credible. Model verification is the process of determining whether a simulation model is appropriate for its intended use. It involves comparing the output of the model to real-world data or expert opinion to determine if the model is accurate and reliable. Verification is important because even if a model is validated and free of errors, it may still not be valid for its intended purpose. Verification involves testing the model under various scenarios and conditions to ensure that it accurately reflects the real-world system it is intended to simulate. To verify our model, multiple steps were performed during the model-building process. Specifically, a series of small-scale unit tests were carried out to ensure that every component of the model was verified. The model trace feature of the Simio software was used extensively for debugging purposes, as it provides a detailed output of variable values at every step of the model. Initially, the routing logic of the simulation was verified, ensuring that students were directed to the appropriate counselor, students requiring additional sessions were scheduled appropriately, and students who had completed their sessions exited the system. To verify this, the model was run one student at a time and their journey was tracked. Additionally, the custom algorithms implemented in the model, such as triage counselor assignment and next session slot allocation, were verified to ensure they were functioning as intended. Detailed outputs were generated for the earliest compatible session for all counselors when a student booked a slot, to confirm that the model was functioning as intended.</p> <p>Simulation model validation is the process of ensuring that a simulation model accurately represents the real-world system it is intended to simulate. Validation is one of the key steps in the simulation model development process and is crucial to ensure the reliability and validity of the simulation results. Validation involves comparing the model's behavior and outputs to actual system data, historical records, or expert opinion to determine if the model is accurately representing the system's behavior. In order to do this, the simulation model was run using Fall 2019 data sets and compared to the reported access time of the system during that period. To perform this experiment, the current scheduling policy being implemented by TAMU CAPS must first be defined. Based on the data, it was found that counselors spend 8% of their time on first-time sessions and 4% on crisis sessions. The experiment was run, and output was obtained for the access time. The box and whisker plot for the simulated expected access time across the 32 replications of the experiment is shown in Figure 10. The average expected access time was 9.3 days, with a half width of 0.42 days. The reported average expected access time during Fall 2019 is also shown in the plot as a red line, and it was found that the simulation output closely matches this value. In addition, upon conducting a <emph>t</emph>-test for the mean access time from the simulation, we fail to reject the null hypothesis that there is a statistically significant difference between the two (<emph>p</emph> value</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>=</mo><mn>0.35</mn></mrow></math> </ephtml> ). Chatterjee et al.[<reflink idref="bib33" id="ref35">33</reflink>] provide comprehensive evidence of model validation and verification.</p> <hd id="AN0183596669-16">Statistical analysis: t-tests</hd> <p>To compare the performance of the different policies, measured by different performance metrics, we conducted a series of <emph>t</emph>-tests to compare each pair of policies to test the null hypothesis that there is no statistical difference between their average performance metric value. More formally, the null and the alternative hypotheses are represented as follows:</p> <p>Graph</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mtable><mtr><mtd><mrow><msub><mrow><mi mathvariant="normal">H</mi></mrow><mn>0</mn></msub></mrow><mo>:</mo><mrow><msub><mrow><mi>μ</mi></mrow><mi mathvariant="normal">A</mi></msub></mrow><mo>=</mo><mrow><msub><mrow><mi>μ</mi></mrow><mi mathvariant="normal">B</mi></msub></mrow></mtd></mtr><mtr><mtd><mrow><msub><mrow><mi mathvariant="normal">H</mi></mrow><mn>1</mn></msub></mrow><mo>:</mo><mrow><msub><mrow><mi>μ</mi></mrow><mi mathvariant="normal">A</mi></msub></mrow><mo>≠</mo><mrow><msub><mrow><mi>μ</mi></mrow><mi mathvariant="normal">B</mi></msub></mrow></mtd></mtr></mtable></math> </ephtml> </p> <p>Where <emph>μ</emph> is the mean of a given performance metric, which is reported by the simulation model. <emph>A</emph> and <emph>B</emph> are the pair of policies that are compared to each other. At a 95% confidence level, we reject the null hypothesis if the <emph>p</emph> value is smaller than.05</p> <p>The following subsections discuss the different <emph>t</emph>-tests conducted to compare the performance of the various group scheduling policies measured by access time, surplus, group waiting time, and group utilization.</p> <hd id="AN0183596669-17">Access time without resource reallocation</hd> <p>We first consider the results presented in "Assessing the effect of group-counseling" section in which groups are introduced without any resource reallocation. Figure 4 illustrates a set of nine policies, leading to a total of 36 tests. The <emph>p</emph> values of those tests are shown in Table 3. Since all <emph>p</emph> values are significantly higher than.05, we do not have enough statistical evidence to reject the null hypothesis. In other words, there is no statistical difference in the average access time of the different policies.</p> <hd id="AN0183596669-18">Surplus</hd> <p>For the surplus results in "Assessing the effect of group-counseling" section, the null hypothesis is that there is no statistical difference in the average surplus of the different policies. Since the policy is fully defined by the number of offered groups and their scheduling time, there are 56 unique policies. Therefore, we only considered the policies where 19 groups are offered. The <emph>p</emph> values of these <emph>t</emph>-tests are shown in Table 4. In contrast to the previous findings, we have observed that policies exhibit a statistically different average surplus when compared to some other policies. For example, there is enough statistical evidence to reject the null hypothesis when comparing the uniform policy to all other policies, except for the fourth week and the sixth week policies.</p> <hd id="AN0183596669-19">Access time with resource reallocation</hd> <p>Similar analysis is done for the average access time after resource reallocation of the different policies. The null hypothesis here is that there is not a statistically significant difference between the average access time of each pair of policies when the number of offered groups is 19. The <emph>p</emph> values of this series of tests are shown in Table 5. On one hand, some policies are similar to each other, such as: the uniform and the fourth week policies, 50-50 and eighth week policies, and second week and fourth week policies. On the other hand, the <emph>p</emph> values of most tests suggest that there is sufficient evidence to reject the null hypothesis. In other words, there is a statistically significant difference between the average access time of most policy-pairs.</p> <hd id="AN0183596669-20">Group waiting time and utilization</hd> <p>The same analysis is done to compare utilization and group waiting time, which are shown in Tables 6 and 7, respectively. Interestingly, the <emph>p</emph> values of all the tests, assuming the number of offered groups is 19, suggest that all policies are statistically different from each other when it comes to group waiting time and utilization.</p> <hd id="AN0183596669-21">Alternative approach</hd> <p>First, we compare the alternative approach, assuming an equivalent number of groups of 27, with all group-offering policies under different number of offered groups. The performance metric in these tests is access time, and the resulting <emph>p</emph> values are shown in Table 8. All <emph>p</emph> values are smaller than.0001, which indicates that, with 99.9% confidence, there is sufficient evidence to reject the null hypothesis. In other words, even with only four groups, group-offering policies have a better impact on access time than directly allocating the equivalent of 27 groups to triage service proportion. Second, we conducted a series of <emph>t</emph>-tests to compare the alternative approach to itself under different number of groups. The <emph>p</emph> values of those tests are shown in Table 9. For example, looking at the pair (<reflink idref="bib12" id="ref36">12</reflink>, 27), the result indicates that we do not have sufficient statistical evidence to reject the null hypothesis. In other words, under the alternative approach, one cannot definitively conclude that reallocating the resources of 27 groups has a bigger impact on access time than reallocating the resources of 12 groups.</p> <p>Graph: Figure 1. Example of two scheduling policies when two group types are provided. The shaded parts represent slots dedicated to group counseling sessions, while the blank parts represent nongroup counseling related activities.</p> <p>Graph: Figure 2. High-level summary of the operational flow at CAPS when group counseling is provided.</p> <p>Graph: Figure 3. Discrete distribution of total number of sessions for patients.</p> <p>Graph: Figure 4. Access time, in the form of box plots, across the different group scheduling policies when the number of offered groups is equal to 19.</p> <p>Graph: Figure 5. Surplus, as a function of the number of offered groups, of ongoing slots over the case of no groups under different group scheduling policies.</p> <p>Graph: Figure 6. Average access time with resource reallocation as a function of the number of offered groups under each of the considered group scheduling policies.</p> <p>Graph: Figure 7. Average group waiting time as a function of the number of offered groups under each of the considered group scheduling policies.</p> <p>Graph: Figure 8. Average group utilization as a function of the number of offered groups under each of the considered group scheduling policies.</p> <p>Graph: Figure 9. Average access time as a function of the number of offered groups under the alternative approach and each of the considered group scheduling policies.</p> <p>Graph: Figure 10. Box plot for expected access time under the currently implemented scheduling policy at TAMU CAPS. The red line shows the actual reported access time by TAMU CAPS. Single, diamond-shaped points are outliers.</p> <p>Table 1. Example of summary table of all group counseling activities when three groups are offered.</p> <p> <ephtml> <table><thead><tr><td>Group ID</td><td>Start date</td><td>Number of sessions</td><td>Frequency (per week)</td><td>Counselor IDs</td><td>Capacity</td></tr></thead><tbody valign="top"><tr><td>1</td><td>August 29, 2022</td><td char=".">4</td><td char=".">0.5</td><td char=".">3, 5</td><td char=".">10</td></tr><tr><td>2</td><td>October 15, 2022</td><td char=".">6</td><td char=".">1</td><td char=".">1, 4</td><td char=".">5</td></tr><tr><td>3</td><td>October 1, 2022</td><td char=".">3</td><td char=".">0.25</td><td char=".">2</td><td char=".">20</td></tr></tbody></table> </ephtml> </p> <p>Table 2. All possible solutions satisfying average group waiting time cutoff of 20 business days.</p> <p> <ephtml> <table><thead><tr><td>Sol. num.</td><td>Policy</td><td>Number of groups</td><td>Group waiting time</td><td>Average group utilization</td><td>Access time % reduction</td></tr></thead><tbody valign="top"><tr><td>1*</td><td>Uniform</td><td char=".">4</td><td char=".">11</td><td char=".">95%</td><td char=".">25%</td></tr><tr><td>2*</td><td>Uniform</td><td char=".">8</td><td char=".">9</td><td char=".">74%</td><td char=".">30%</td></tr><tr><td><italic>3</italic></td><td><italic>Uniform</italic></td><td><italic>12</italic></td><td><italic>8</italic></td><td><italic>60%</italic></td><td><italic>35%</italic></td></tr><tr><td><italic>4</italic></td><td><italic>Uniform</italic></td><td><italic>16</italic></td><td><italic>8</italic></td><td><italic>48%</italic></td><td><italic>37%</italic></td></tr><tr><td><italic>5</italic></td><td><italic>Uniform</italic></td><td><italic>19</italic></td><td><italic>7</italic></td><td><italic>42%</italic></td><td><italic>35%</italic></td></tr><tr><td><italic>6</italic></td><td><italic>Uniform</italic></td><td><italic>23</italic></td><td><italic>7</italic></td><td><italic>36%</italic></td><td><italic>38%</italic></td></tr><tr><td><italic>7</italic></td><td><italic>Uniform</italic></td><td><italic>27</italic></td><td><italic>7</italic></td><td><italic>30%</italic></td><td><italic>39%</italic></td></tr><tr><td><italic>8</italic></td><td><italic>2nd week</italic></td><td><italic>4</italic></td><td><italic>4</italic></td><td><italic>65%</italic></td><td><italic>25%</italic></td></tr><tr><td><italic>9</italic></td><td><italic>2nd week</italic></td><td><italic>8</italic></td><td><italic>4</italic></td><td><italic>31%</italic></td><td><italic>26%</italic></td></tr><tr><td><italic>10</italic></td><td><italic>2nd week</italic></td><td><italic>12</italic></td><td><italic>4</italic></td><td><italic>22%</italic></td><td><italic>27%</italic></td></tr><tr><td><italic>11</italic></td><td><italic>2nd week</italic></td><td><italic>16</italic></td><td><italic>4</italic></td><td><italic>16%</italic></td><td><italic>22%</italic></td></tr><tr><td><italic>12</italic></td><td><italic>2nd week</italic></td><td><italic>19</italic></td><td><italic>4</italic></td><td><italic>14%</italic></td><td><italic>26%</italic></td></tr><tr><td><italic>13</italic></td><td><italic>2nd week</italic></td><td><italic>23</italic></td><td><italic>4</italic></td><td><italic>12%</italic></td><td><italic>27%</italic></td></tr><tr><td><italic>14</italic></td><td><italic>2nd week</italic></td><td><italic>27</italic></td><td><italic>4</italic></td><td><italic>10%</italic></td><td><italic>31%</italic></td></tr><tr><td>15*</td><td>4th week</td><td char=".">4</td><td char=".">12</td><td char=".">100%</td><td char=".">25%</td></tr><tr><td>16*</td><td>4th week</td><td char=".">8</td><td char=".">9</td><td char=".">76%</td><td char=".">27%</td></tr><tr><td><italic>17</italic></td><td><italic>4th week</italic></td><td><italic>12</italic></td><td><italic>9</italic></td><td><italic>51%</italic></td><td><italic>31%</italic></td></tr><tr><td><italic>18</italic></td><td><italic>4th week</italic></td><td><italic>16</italic></td><td><italic>9</italic></td><td><italic>36%</italic></td><td><italic>33%</italic></td></tr><tr><td><italic>19</italic></td><td><italic>4th week</italic></td><td><italic>19</italic></td><td><italic>9</italic></td><td><italic>31%</italic></td><td><italic>33%</italic></td></tr><tr><td><italic>20</italic></td><td><italic>4th week</italic></td><td><italic>23</italic></td><td><italic>9</italic></td><td><italic>26%</italic></td><td><italic>33%</italic></td></tr><tr><td><italic>21</italic></td><td><italic>4th week</italic></td><td><italic>27</italic></td><td><italic>9</italic></td><td><italic>23%</italic></td><td><italic>35%</italic></td></tr><tr><td>22*</td><td>6th week</td><td char=".">8</td><td char=".">16</td><td char=".">100%</td><td char=".">31%</td></tr><tr><td>23*</td><td>6th week</td><td char=".">12</td><td char=".">14</td><td char=".">75%</td><td char=".">34%</td></tr><tr><td><italic>24</italic></td><td><italic>6th week</italic></td><td><italic>16</italic></td><td><italic>14</italic></td><td><italic>58%</italic></td><td><italic>37%</italic></td></tr><tr><td><italic>25</italic></td><td><italic>6th week</italic></td><td><italic>19</italic></td><td><italic>14</italic></td><td><italic>48%</italic></td><td><italic>39%</italic></td></tr><tr><td><italic>26</italic></td><td><italic>6th week</italic></td><td><italic>23</italic></td><td><italic>14</italic></td><td><italic>40%</italic></td><td><italic>38%</italic></td></tr><tr><td><italic>27</italic></td><td><italic>6th week</italic></td><td><italic>27</italic></td><td><italic>14</italic></td><td><italic>35%</italic></td><td><italic>41%</italic></td></tr><tr><td><bold>28*</bold></td><td><bold>8th week</bold></td><td char="."><bold>16</bold></td><td char="."><bold>19</bold></td><td char="."><bold>77%</bold></td><td char="."><bold>44%</bold></td></tr><tr><td><italic>29</italic></td><td><italic>8th week</italic></td><td><italic>19</italic></td><td><italic>19</italic></td><td><italic>67%</italic></td><td><italic>43%</italic></td></tr><tr><td><italic>30</italic></td><td><italic>8th week</italic></td><td><italic>23</italic></td><td><italic>19</italic></td><td><italic>56%</italic></td><td><italic>42%</italic></td></tr><tr><td><italic>31</italic></td><td><italic>8th week</italic></td><td><italic>27</italic></td><td><italic>19</italic></td><td><italic>46%</italic></td><td><italic>44%</italic></td></tr><tr><td><italic>32</italic></td><td><italic>50-50 split</italic></td><td><italic>27</italic></td><td><italic>20</italic></td><td><italic>61%</italic></td><td><italic>50%</italic></td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note.</emph><emph>Italics</emph> indicate solutions with average group utilization below 70%. Underlined numbers followed by an asterisk (*) denote solutions with average group utilization above 70%. The best-performing solution is shown in <bold>bold</bold>.</p> <p>Table 3. Results of <emph>t</emph>-tests of the policies' resulting access time, when the number of offered groups is 19, without resource reallocation.</p> <p> <ephtml> <table><thead><tr><td>Uniform</td><td>50-50</td><td>2nd</td><td>4th</td><td>6th</td><td>8th</td><td>10th</td><td>11th</td></tr></thead><tbody valign="top"><tr><td>No groups</td><td char=".">0.65</td><td char=".">0.43</td><td char=".">0.26</td><td char=".">0.37</td><td char=".">0.49</td><td char=".">0.58</td><td char=".">0.47</td><td char=".">0.47</td></tr><tr><td>Uniform</td><td>–</td><td char=".">0.75</td><td char=".">0.50</td><td char=".">0.67</td><td char=".">0.81</td><td char=".">0.91</td><td char=".">0.77</td><td char=".">0.78</td></tr><tr><td>50-50</td><td>–</td><td char=".">0.69</td><td char=".">0.91</td><td char=".">0.95</td><td char=".">0.85</td><td char=".">0.99</td><td char=".">0.99</td></tr><tr><td>2nd</td><td>–</td><td char=".">0.78</td><td char=".">0.67</td><td char=".">0.59</td><td char=".">0.73</td><td char=".">0.71</td></tr><tr><td>4th</td><td>–</td><td char=".">0.87</td><td char=".">0.77</td><td char=".">0.92</td><td char=".">0.91</td></tr><tr><td>6th</td><td>–</td><td char=".">0.90</td><td char=".">0.95</td><td char=".">0.97</td></tr><tr><td>8th</td><td>–</td><td char=".">0.86</td><td char=".">0.87</td></tr><tr><td>10th</td><td>–</td><td char=".">0.98</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note.</emph> Values represent the <emph>p</emph> value of the test.</p> <p>Table 4. Results of <emph>t</emph>-tests of the policies' surplus when the number of offered groups is 19.</p> <p> <ephtml> <table><thead><tr><td>50-50</td><td>2nd</td><td>4th</td><td>6th</td><td>8th</td><td>10th</td><td>11th</td></tr></thead><tbody valign="top"><tr><td>Uniform</td><td char=".">0.00***</td><td char=".">0.00**</td><td char=".">0.40</td><td char=".">0.14</td><td char=".">0.00**</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>50-50</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.01*</td><td char=".">0.48</td><td char=".">0.33</td><td char=".">0.04*</td></tr><tr><td>2nd</td><td>–</td><td char=".">0.02*</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>4th</td><td>–</td><td char=".">0.02*</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>6th</td><td>–</td><td char=".">0.02*</td><td char=".">0.00**</td><td char=".">0.00***</td></tr><tr><td>8th</td><td>–</td><td char=".">0.06</td><td char=".">0.00**</td></tr><tr><td>10th</td><td>–</td><td char=".">0.21</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note.</emph> Values represent the <emph>p</emph> value of the test.</item> <item>4 *<emph>p</emph> <.05. **<emph>p</emph> <.001. ***<emph>p</emph> <.0001.</item> </ulist> <p>Table 5. Results of <emph>t</emph>-tests of the policies' resulting access time when the number of offered groups is 19.</p> <p> <ephtml> <table><thead><tr><td>50-50</td><td>2nd</td><td>4th</td><td>6th</td><td>8th</td><td>10th</td><td>11th</td></tr></thead><tbody valign="top"><tr><td>Uniform</td><td char=".">0.00***</td><td char=".">0.03*</td><td char=".">0.80</td><td char=".">0.01**</td><td char=".">0.00**</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>50-50</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.02*</td><td char=".">0.17</td><td char=".">0.19</td><td char=".">0.01*</td></tr><tr><td>2nd</td><td>–</td><td char=".">0.08</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>4th</td><td>–</td><td char=".">0.00**</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>6th</td><td>–</td><td char=".">0.35</td><td char=".">0.00**</td><td char=".">0.00***</td></tr><tr><td>8th</td><td>–</td><td char=".">0.02*</td><td char=".">0.00**</td></tr><tr><td>10th</td><td>–</td><td char=".">0.24</td></tr></tbody></table> </ephtml> </p> <ulist> <item>5 <emph>Note.</emph> Values represent the rounded <emph>p</emph> value of the test.</item> <item>6 *<emph>p</emph> <.05. **<emph>p</emph> <.001. ***<emph>p</emph> <.0001.</item> </ulist> <p>Table 6. Results of <emph>t</emph>-tests of the policies' resulting group waiting time when the number of offered groups is 19.</p> <p> <ephtml> <table><thead><tr><td>50-50</td><td>2nd</td><td>4th</td><td>6th</td><td>8th</td><td>10th</td><td>11th</td></tr></thead><tbody valign="top"><tr><td>Uniform</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>50-50</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.01*</td><td char=".">0.00***</td></tr><tr><td>2nd</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>4th</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>6th</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>8th</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>10th</td><td>–</td><td char=".">0.00***</td></tr></tbody></table> </ephtml> </p> <ulist> <item>7 <emph>Note.</emph> Values represent the rounded <emph>p</emph> value of the test.</item> <item>8 *<emph>p</emph> <.05. **<emph>p</emph> <.001. ***<emph>p</emph> <.0001.</item> </ulist> <p>Table 7. Results of <emph>t</emph>-tests of the policies' utilization when the number of offered groups is 19.</p> <p> <ephtml> <table><thead><tr><td>50-50</td><td>2nd</td><td>4th</td><td>6th</td><td>8th</td><td>10th</td><td>11th</td></tr></thead><tbody valign="top"><tr><td>Uniform</td><td char=".">0.00***</td><td char=".">0.00**</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00**</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>50-50</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00**</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>2nd</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>4th</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>6th</td><td>–</td><td char=".">0.00***</td><td char=".">0.00**</td><td char=".">0.00***</td></tr><tr><td>8th</td><td>–</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>10th</td><td>–</td><td char=".">0.00***</td></tr></tbody></table> </ephtml> </p> <ulist> <item>9 <emph>Note.</emph> Values represent the <emph>p</emph> value of the test.</item> <item>10 *<emph>p</emph> <.05. **<emph>p</emph> <.001. ***<emph>p</emph> <.0001.</item> </ulist> <p>Table 8. Results of <emph>t</emph>-tests between the alternative approach, with an equivalent number of groups of 27, and groups-offering policies under different number of offered groups.<sups>a</sups></p> <p> <ephtml> <table><thead><tr><td>No. of groups</td><td>Uniform</td><td>50-50</td><td>2nd</td><td>4th</td><td>6th</td><td>8th</td><td>10th</td><td>11th</td></tr></thead><tbody valign="top"><tr><td>4</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>8</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>12</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>16</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>19</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>23</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>27</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td><td char=".">0.00***</td></tr></tbody></table> </ephtml> </p> <p>11 <sups>a</sups>*<emph>p</emph> <.05. **<emph>p</emph> <.001. ***<emph>p</emph> <.0001.</p> <p>Table 9. Results of <emph>t</emph>-tests, comparing the alternative policy under different equivalent number of groups.<sups>a</sups></p> <p> <ephtml> <table><thead><tr><td char=".">8</td><td char=".">12</td><td char=".">16</td><td char=".">19</td><td char=".">23</td><td char=".">27</td></tr></thead><tbody valign="top"><tr><td>4</td><td char=".">0.03*</td><td char=".">0.00*</td><td char=".">0.00*</td><td char=".">0.00**</td><td char=".">0.00***</td><td char=".">0.00***</td></tr><tr><td>8</td><td>–</td><td char=".">0.15</td><td char=".">0.28</td><td char=".">0.08</td><td char=".">0.01*</td><td char=".">0.00*</td></tr><tr><td>12</td><td>–</td><td char=".">0.79</td><td char=".">0.62</td><td char=".">0.18</td><td char=".">0.07</td></tr><tr><td>16</td><td>–</td><td char=".">0.48</td><td char=".">0.13</td><td char=".">0.05</td></tr><tr><td>19</td><td>–</td><td char=".">0.43</td><td char=".">0.26</td></tr><tr><td>23</td><td>–</td><td char=".">0.75</td></tr></tbody></table> </ephtml> </p> <p>12 <sups>a</sups>*<emph>p</emph> <.05. **<emph>p</emph> <.001. ***<emph>p</emph> <.0001.</p> <ref id="AN0183596669-22"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> National Institute of Mental Health. 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  Data: A Data-Driven Simulation Approach to Quantify the Effect of Group Counseling on System Performance of College Counseling Centers
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  Data: <searchLink fieldCode="AR" term="%22Youssef+Hebaish%22">Youssef Hebaish</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3209-2790">0000-0003-3209-2790</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sohom+Chatterjee%22">Sohom Chatterjee</searchLink><br /><searchLink fieldCode="AR" term="%22James+Deegear%22">James Deegear</searchLink><br /><searchLink fieldCode="AR" term="%22Miles+Rucker%22">Miles Rucker</searchLink><br /><searchLink fieldCode="AR" term="%22Hrayer+Aprahamian%22">Hrayer Aprahamian</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8750-2366">0000-0002-8750-2366</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lewis+Ntaimo%22">Lewis Ntaimo</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+American+College+Health%22"><i>Journal of American College Health</i></searchLink>. 2025 73(3):1240-1254.
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  Data: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 15
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  Data: 2025
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Group+Counseling%22">Group Counseling</searchLink><br /><searchLink fieldCode="DE" term="%22School+Counseling%22">School Counseling</searchLink><br /><searchLink fieldCode="DE" term="%22Guidance+Centers%22">Guidance Centers</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+Allocation%22">Resource Allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Counseling+Services%22">Counseling Services</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Patients%22">Patients</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Texas%22">Texas</searchLink>
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  Data: 10.1080/07448481.2023.2252916
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  Data: 0744-8481<br />1940-3208
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: To investigate the effectiveness, from a system's perspective, of offering group counseling options in college counseling centers. Methods: We achieve this through a data-driven simulation-based approach with the aim of providing administrators with a quantitative tool that informs their decision-making process. Results: Our simulation experiments reveal that offering group counseling options without resource reallocation does not have the desired positive impact on the system's performance. However, with resource reallocation, our results demonstrate that the introduction of group counseling options can significantly improve the performance of the system by as much as 40%. Conclusions: Group counseling options, coupled with proper resource reallocation strategies, are effective in reducing access time of first-time patients by as much as 40%. The effect of group counseling is highly dependent on both the number of offered groups as well as their scheduling policy. Scheduling policies have to be scrutinized in light of their resulting group waiting time and resource-utilization efficiency.
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        PageCount: 15
        StartPage: 1240
    Subjects:
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        Type: general
      – SubjectFull: School Counseling
        Type: general
      – SubjectFull: Guidance Centers
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