Associations Between Personality and Mental Health Among Royal Canadian Mounted Police Cadets
Bibliographic record
Abstract
Abstract Royal Canadian Mounted Police (RCMP) report frequent exposures to diverse potentially psychological traumatic events (PPTEs) that can lead to symptoms of posttraumatic stress disorder (PTSD) and other mental health disorders. Personality traits may partially inform the substantial mental health challenges reported by serving RCMP. The current study examines associations between HEXACO personality factor and facet-level dimensions and mental health disorders of RCMP cadets starting the Cadet Training Program (CTP). RCMP cadets (n = 772) starting the CTP self-reported sociodemographics, personality, and mental health disorder symptoms. Emotionality was associated with MDD, GAD, and SAD (AORs ranged from 6.23 to 10.22). Extraversion and Agreeableness were inversely associated with MDD, GAD, and SAD (AORs ranged from 0.0159 to 0.43), whereas Openness to Experience was inversely associated with SAD (AOR = 0.36). Several facet-level personality dimensions were associated with mental health disorders. Inconsistent differences were observed between men and women for relationships between personality factors, facets, and positive screenings for mental disorders. The relationship patterns allude to possible risk and resilience factors associated with personality factors and facets. Early training, interventions, and resources tailored to cadet personality factors and facets might reduce risk and bolster mental health resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".