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Record W4316193242 · doi:10.1177/00938548221143533

“I’ve Seen More Dead People than I thought I Would”: Vicarious Trauma Exposure among Police Support Personnel

2023· article· en· W4316193242 on OpenAlexaffabout
Laura Huey, Mark Norman, Rosemary Ricciardelli, Dale Spencer

Bibliographic record

VenueCriminal Justice and Behavior · 2023
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsMemorial University of NewfoundlandCarleton UniversityMcMaster UniversityWestern University
Fundersnot available
KeywordsMental healthOccupational safety and healthPsychologyTollSuicide preventionPoison controlMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

Civilian administrative assistants who work for the Royal Canadian Mounted Police, known as Detachment Services Assistants (DSAs), are frequently exposed to materials and/or experiences that are potentially psychologically traumatic. Drawing from 49 semi-structured interviews with DSAs, we analyze how these civilian personnel experience exposure to potentially psychologically traumatic events, most notably, vicarious trauma. Specifically, we overview the types of exposure to potentially psychologically traumatic events and materials experienced by DSAs, including the impact of incidents involving children, and the occupational duties through which these exposures occur; we unpack the nuances and variability in DSAs’ occupational work, which informs such exposures; and we draw from DSAs’ experiences to offer recommendations for ameliorating the mental health toll of civilian police work. The study adds to the limited academic literature on the occupational and mental health experiences of civilian personnel, who serve a vital, but underrecognized, role in supporting police operations.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.087
GPT teacher head0.392
Teacher spread0.305 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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