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Measuring resilience using language modeling: A computational approach to observing resilience.

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Title: Measuring resilience using language modeling: A computational approach to observing resilience.
Authors: Mahwish, Syeda (AUTHOR), Boyd, Ryan L. (AUTHOR), Varadarajan, Vasudha (AUTHOR), Kotov, Roman (AUTHOR), Luft, Benjamin J. (AUTHOR), Schwartz, H. Andrew (AUTHOR), Clouston, Sean A. P. (AUTHOR)
Source: Journal of Traumatic Stress. Jun2026, Vol. 39 Issue 3, p376-389. 14p.
Subjects: Psychological resilience, Natural language processing, Post-traumatic stress disorder, Archetypes, September 11 Terrorist Attacks, 2001, Psychological tests, Modeling languages (Computer science), Content analysis
Abstract: We developed resilience using language modeling (ReLM) to measure resilience in language through a novel natural language processing approach called archetype analysis. Our model conceptualizes resilience as a process of maintaining healthy functioning after an adverse event. ReLM is theoretically synthesized through nine facets of resilience reviewed from various sources as reflected in language that captures its dynamic capacity: optimism, sense of social support, emotional maturity, uncertainty tolerance, flexible mindset, coping toolkit, cognitive reappraisal, belief in a higher power, and continued activities of daily living. ReLM uses a language model to embed language in a semantic space, with cosine similarity to each facet's prototype statements calculated to quantify a theoretically derived facet score. We applied ReLM to 1,859 voicemails collected from 211 responders to the September 11, 2001, World Trade Center terrorist attacks. Principal component analysis on training and test sets identified a single latent factor from the facet scores, λ = 5.02 (56% variance explained), and measurement invariance testing confirmed scalar invariance across training and test subsets, Δχ2(8) = 8.89, p =.352, indicating ReLM scores reflected the same underlying construct in both sets. A one‐way analysis of variance showed significant differences in posttraumatic stress disorder (PTSD) symptom trajectories across resilience quartiles, F(3, 169) = 5.18, p =.002, with high resilience showing the largest improvements in PTSD after 4 years (M = −0.212). Using an archetype‐based language model, ReLM offers a theoretically grounded approach to measuring resilience through natural language, capturing psychological processes in narratives, and enabling dynamic assessment. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Traumatic Stress is the property of Wiley-Blackwell 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: Psychology and Behavioral Sciences Collection
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PubType: Academic Journal
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  Data: Measuring resilience using language modeling: A computational approach to observing resilience.
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  Data: <searchLink fieldCode="AR" term="%22Mahwish%2C+Syeda%22">Mahwish, Syeda</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Boyd%2C+Ryan+L%2E%22">Boyd, Ryan L.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Varadarajan%2C+Vasudha%22">Varadarajan, Vasudha</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kotov%2C+Roman%22">Kotov, Roman</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luft%2C+Benjamin+J%2E%22">Luft, Benjamin J.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schwartz%2C+H%2E+Andrew%22">Schwartz, H. Andrew</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Clouston%2C+Sean+A%2E+P%2E%22">Clouston, Sean A. P.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Traumatic+Stress%22">Journal of Traumatic Stress</searchLink>. Jun2026, Vol. 39 Issue 3, p376-389. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Psychological+resilience%22">Psychological resilience</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Post-traumatic+stress+disorder%22">Post-traumatic stress disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Archetypes%22">Archetypes</searchLink><br /><searchLink fieldCode="DE" term="%22September+11+Terrorist+Attacks%2C+2001%22">September 11 Terrorist Attacks, 2001</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+tests%22">Psychological tests</searchLink><br /><searchLink fieldCode="DE" term="%22Modeling+languages+%28Computer+science%29%22">Modeling languages (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Content+analysis%22">Content analysis</searchLink>
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  Data: We developed resilience using language modeling (ReLM) to measure resilience in language through a novel natural language processing approach called archetype analysis. Our model conceptualizes resilience as a process of maintaining healthy functioning after an adverse event. ReLM is theoretically synthesized through nine facets of resilience reviewed from various sources as reflected in language that captures its dynamic capacity: optimism, sense of social support, emotional maturity, uncertainty tolerance, flexible mindset, coping toolkit, cognitive reappraisal, belief in a higher power, and continued activities of daily living. ReLM uses a language model to embed language in a semantic space, with cosine similarity to each facet's prototype statements calculated to quantify a theoretically derived facet score. We applied ReLM to 1,859 voicemails collected from 211 responders to the September 11, 2001, World Trade Center terrorist attacks. Principal component analysis on training and test sets identified a single latent factor from the facet scores, λ = 5.02 (56% variance explained), and measurement invariance testing confirmed scalar invariance across training and test subsets, Δχ2(8) = 8.89, p =.352, indicating ReLM scores reflected the same underlying construct in both sets. A one‐way analysis of variance showed significant differences in posttraumatic stress disorder (PTSD) symptom trajectories across resilience quartiles, F(3, 169) = 5.18, p =.002, with high resilience showing the largest improvements in PTSD after 4 years (M = −0.212). Using an archetype‐based language model, ReLM offers a theoretically grounded approach to measuring resilience through natural language, capturing psychological processes in narratives, and enabling dynamic assessment. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Traumatic Stress is the property of Wiley-Blackwell 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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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1002/jts.70046
    Languages:
      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 376
    Subjects:
      – SubjectFull: Psychological resilience
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Post-traumatic stress disorder
        Type: general
      – SubjectFull: Archetypes
        Type: general
      – SubjectFull: September 11 Terrorist Attacks, 2001
        Type: general
      – SubjectFull: Psychological tests
        Type: general
      – SubjectFull: Modeling languages (Computer science)
        Type: general
      – SubjectFull: Content analysis
        Type: general
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      – TitleFull: Measuring resilience using language modeling: A computational approach to observing resilience.
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            NameFull: Mahwish, Syeda
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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