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Record W4283806950 · doi:10.1007/s10879-022-09547-6

Interdisciplinary Trauma-focused Therapy and Return-to-work Support for A Police Officer with Work-related PTSD: A Case Study

2022· article· en· W4283806950 on OpenAlexaff
Iris Torchalla, John B. Killoran

Bibliographic record

VenueJournal of Contemporary Psychotherapy · 2022
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsOfficerContext (archaeology)Psychological interventionPsychologyIntervention (counseling)Compensation (psychology)Occupational stressPsychiatryClinical psychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Police officers carry a high risk of exposure to traumatic events in their everyday work duties and are at an increased risk for work-related posttraumatic stress disorder (PTSD). Practitioners lack clear guidance on how to support these individuals to facilitate both mental health recovery and return to work, particularly for those receiving treatment in the context of a claim with a workers' compensation board. The following case study describes the treatment of a female police officer who had experienced numerous traumatic events over the course of her career, and subsequently filed a claim with the workers' compensation board of British Columbia. She was referred to an interdisciplinary program that involved both psychology and occupational therapy interventions, including a trauma-focused cognitive behaviour therapy intervention followed by a gradual return to work. The outcome suggests that intensive, interdisciplinary trauma-focused treatment is a promising approach for supporting police officers with their recovery and return to work.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.407
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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