MétaCan
Menu
Back to cohort
Record W4392109219 · doi:10.1080/15614263.2024.2318209

Mental health disorder symptoms among serving Royal Canadian Mounted Police

2024· article· en· W4392109219 on OpenAlexaffabout
R. Nicholas Carleton, Laleh Jamshidi, Jolan Nisbet, Robyn E. Shields, Katie L. Andrews

Bibliographic record

VenuePolice Practice and Research · 2024
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsCanadian Institute for Public Safety Research and TreatmentUniversity of Regina
Fundersnot available
KeywordsMental healthStressorPublic healthPandemicPsychiatryMedicineOccupational safety and healthPopulationPosttraumatic stressCoronavirus disease 2019 (COVID-19)PsychologyEnvironmental healthNursingDisease

Abstract

fetched live from OpenAlex

The Royal Canadian Mounted Police (RCMP) experience extraordinary exposures to diverse occupational stressors, potentially exacerbated by systemic stressors (e.g., public calls for pervasive organizational changes, the COVID-19 pandemic, natural disasters). The current study was designed to assess the mental health of currently serving RCMP (n = 1348) who completed an online survey from June 2022 to February 2023. The positive screening prevalence for any mental health disorder was higher (ps<.05) for current participants (64.7%) than previously reported for RCMP members (50.2%), diverse public safety personnel (44.5%), and the general population diagnostic prevalence (10.1%). Women were less likely (ps<.05) to screen positive for posttraumatic stress disorder and alcohol use disorder than men participants. RCMP participants evidenced substantially more mental health challenges than previous assessments, underscoring urgent and growing needs for proactive, ongoing, evidence-based supports for RCMP mental health, at the individual, organizational, and structural levels.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.048
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.493
Teacher spread0.420 · 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 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

Citations14
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuePolice Practice and ResearchSame topicPosttraumatic Stress Disorder ResearchFrench-language works237,207