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Development of competency models for assessors and simulators in high-stakes selection processes.

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Title: Development of competency models for assessors and simulators in high-stakes selection processes.
Authors: Patterson, Fiona1,2 (AUTHOR) f.patterson@workpsychologygroup.com, Zibarras, Lara3 (AUTHOR), Kerrin, Maire1 (AUTHOR), Lopes, Safiatu1 (AUTHOR), Price, Roger4 (AUTHOR)
Source: Medical Teacher. Dec2014, Vol. 36 Issue 12, p1082-1085. 4p.
Subject Terms: *Professional education, *Qualitative research, *Job performance, *School entrance requirements, Interviewing, Medical specialties & specialists, Study & teaching of medicine, Quality assurance, Questionnaires, Statistical sampling, Descriptive statistics
Geographic Terms: United Kingdom
Abstract: Background: Selection for entry into UK medical specialty training is a high-stakes, high-volume process. For selection into General Practice, a large number of assessors and simulators are involved in the delivery of the selection centre, which represents the final stage of selection. Aim: In order to standardize and quality-assure assessor and simulator involvement in the process, we developed two competency models outlining the knowledge, skills and attributes associated with each role using a previously validated job analysis methodology. Results: The final qualitative analysis resulted in two competency models, each encompassing eight competency domains. In general, results from a validation questionnaire demonstrated positive feedback from various regional recruitment leads in the UK ( n = 14). Conclusion: Both models are currently being used in practice for quality assurance and training purposes. We conclude that the competency models can be used in three ways: (1) recruiting assessors/simulators; (2) in measuring performance of assessors/simulators and highlighting areas for potential development; and (3) they can be used for training assessors/simulators. [ABSTRACT FROM AUTHOR]
Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Development of competency models for assessors and simulators in high-stakes selection processes.
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  Data: <searchLink fieldCode="JN" term="%22Medical+Teacher%22">Medical Teacher</searchLink>. Dec2014, Vol. 36 Issue 12, p1082-1085. 4p.
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  Data: Background: Selection for entry into UK medical specialty training is a high-stakes, high-volume process. For selection into General Practice, a large number of assessors and simulators are involved in the delivery of the selection centre, which represents the final stage of selection. Aim: In order to standardize and quality-assure assessor and simulator involvement in the process, we developed two competency models outlining the knowledge, skills and attributes associated with each role using a previously validated job analysis methodology. Results: The final qualitative analysis resulted in two competency models, each encompassing eight competency domains. In general, results from a validation questionnaire demonstrated positive feedback from various regional recruitment leads in the UK ( n = 14). Conclusion: Both models are currently being used in practice for quality assurance and training purposes. We conclude that the competency models can be used in three ways: (1) recruiting assessors/simulators; (2) in measuring performance of assessors/simulators and highlighting areas for potential development; and (3) they can be used for training assessors/simulators. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2014
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