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Record W4400983201 · doi:10.1037/tra0001753

Sexual victimization among Royal Canadian Mounted Police.

2024· article· en· W4400983201 on OpenAlexafffundabout
Juliana M. B. Khoury, Katie L. Andrews, Amber J. Fletcher, Laleh Jamshidi, Gordon J. G. Asmundson, Taylor A. Teckchandani, R. Nicholas Carleton

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of ReginaCanadian Institute for Public Safety Research and Treatment
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySexual assaultCriminologySex offenseHuman factors and ergonomicsPoison controlComputer securitySocial psychologyMedical emergencySexual abuseMedicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study was designed to provide the first in-depth, academically peer-reviewed assessment of sexual victimization among Royal Canadian Mounted Police (RCMP). METHOD: = 1,324; 76.5% men) completed the self-report survey. RESULTS: = 282), with a greater proportion of women reporting being sexually harassed during the CTP and while on duty. CONCLUSIONS: RCMP cadets appear to be sexually assaulted and sexually harassed less frequently than Canadian university and military college students, whereas RCMP appear to be sexually assaulted more often while on duty than Canadian men and women in the general population while at work; however, direct comparisons are problematic because of differing frames for questions and time spans. The current results help quantify sexual victimization among RCMP, which can support ongoing and novel prevention and intervention strategies. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.165
GPT teacher head0.527
Teacher spread0.362 · 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 designNot applicable
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
Published2024
Admission routes3
Has abstractyes

Explore more

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