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Record W4413250133 · doi:10.1108/pijpsm-09-2024-0160

Emotion regulation, secondary traumatic stress and psychological health at work among child sexual and physical exploitation police investigators

2025· article· en· W4413250133 on OpenAlexaffabout
Audrey Potz, Julie Maheux, Annie Gendron

Bibliographic record

VenuePolicing An International Journal · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPsychologyPath analysis (statistics)PopulationDistressClinical psychologyScale (ratio)Medicine

Abstract

fetched live from OpenAlex

Purpose This study aims to examine the association between emotion regulation, the psychological health at work (PHW) and secondary traumatic stress (STS) of child sexual and physical exploitation investigators (CSPEIs). Design/methodology/approach A path analysis model was tested with 73 Quebec CSPEIs. The instruments included the Difficulties in Emotion Regulation Scale (DERS), the STS subscale of the professional quality of life scale – 5 (ProQOL) and the questionnaire on workplace psychological distress (WPD) and psychological well-being at work (PWBW). Findings The path analysis model revealed that the DERS explains 31.40% of the variance of the STS, 36.5% of the variance of the WPD and 20.9% of the variance of the PWBW. These results indicate that fewer emotion dysregulation is associated with better PHW. Few studies have examined these links in a population of investigators specialized in child sexual crimes. Originality/value This study therefore offers an innovative contribution to the understanding of psychological health among those working in contexts with a high emotional charge. The results support the importance of implementing emotion regulation training in police environments in order to prevent psychological distress among these workers and to improve their well-being at work. A better understanding of these skills can lead to effective prevention strategies, thereby improving the quality of life of professionals and the safety of society.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.365
Teacher spread0.326 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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