A Qualitative Analysis of the Mental Health Training and Educational Needs of Firefighters, Paramedics, and Public Safety Communicators in Canada
Bibliographic record
Abstract
Background-Public safety personnel (PSP) are at heightened risk of developing mental health challenges due to exposures to diverse stressors including potentially psychologically traumatic experiences. An increased focus on protecting PSP mental health has prompted demand for interventions designed to enhance resilience. While hundreds of available interventions are aimed to improve resilience and protect PSPs' mental health, research evidence regarding intervention effectiveness remains sparse. Methods-Focus groups with PSP elicited a discussion of psychoeducational program content, preferred modes of program delivery, when such training should occur, and to whom it ought to be targeted. Results-The results of thematic analyses suggest that PSP participants feel that contemporary approaches to improving mental health and resilience are lacking. While welcomed, the provision of sporadic one-off mental health and resilience programs by organizations was seen as insufficient, and the available organizational mental health supports were perceived as being questionable. The available programs also left participants feeling insufficiently prepared to deal with personal mental health problems and in discussing mental health concerns with co-workers. Conclusions-Participants reported needing more engaging methods for delivering information, career-long mental health knowledge acquisition, and a systems approach to improve the workplace culture, particularly regarding mental health.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".