Mental health training, attitudes toward support, and screening positive for mental disorders
Bibliographic record
Abstract
Public Safety Personnel (PSP; e.g. correctional workers, dispatchers, firefighters, paramedics, police) are frequently exposed to potentially traumatic events (PTEs). Several mental health training program categories (e.g. critical incident stress management (CISM), debriefing, peer support, psychoeducation, mental health first aid, Road to Mental Readiness [R2MR]) exist as efforts to minimize the impact of exposures, often using cognitive behavioral therapy model content, but with limited effectiveness research. The current study assessed PSP perceptions of access to professional (i.e. physicians, psychologists, psychiatrists, employee assistance programs, chaplains) and non-professional (i.e. spouse, friends, colleagues, leadership) support, and associations between training and mental health. Participants included 4,020 currently serving PSP participants. Data were analyzed using cross-tabulations and logistic regressions. Most PSP reported access to professional and non-professional support; nevertheless, most would first access a spouse (74%) and many would never, or only as a last resort, access professional support (43–60%) or PSP leaders (67%). Participation in any mental health training category was associated with lower (p < .01) rates for some, but not all, mental disorders, with no robust differences across categories. Revisions to training programs may improve willingness to access professional support; in the interim, training and support for PSP spouses and leaders may also be beneficial.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".