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Record W3110263114 · doi:10.1080/15614263.2020.1848564

Post-traumatic effects in policing: exploring disclosure, coping and social support

2020· article· en· W3110263114 on OpenAlexaffabout
Marian Pitel, Grace B. Ewles, Peter A. Hausdorf, Cole D. J. Heffren

Bibliographic record

VenuePolice Practice and Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCoping (psychology)OfficerPsychologySocial supportFeelingDistressPsychological distressSocial psychologyClinical psychologyPsychiatryMental healthPolitical science

Abstract

fetched live from OpenAlex

This study explored the relationships between traumatic events experienced at work and police officer distress disclosure, enacted coping, including social support seeking, and impairment. A total of 266 web surveys were completed by sworn officers from a large municipal police force in Canada with 76 reporting having experienced at least one traumatic event at work in the year prior to the survey. The most significant finding was the relationship between officers feeling comfortable disclosing distressing personal information and seeking social support from others, although social support did not relate to their impairment. Reported attempts to cope on their own (self-coping) and to avoid the issue (avoidant coping) were positively related to impairment suggesting that these forms of coping are less effective for police officer work-related trauma. Implications for future research and practice for coping and social support in police occupations are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
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 head0.309
GPT teacher head0.502
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2020
Admission routes2
Has abstractyes

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