A Qualitative Evaluation of the Before Operational Stress Program: A Pan‐Canadian Study of Mental Health Training for Frontline Public Safety Personnel and Healthcare Providers
Bibliographic record
Abstract
Public safety personnel (PSP) and healthcare providers (HCP) are routinely exposed to potentially psychologically traumatic events and are at increased risk of developing mental disorder symptoms and posttraumatic stress injuries (PTSI). The Before Operational Stress program (BOS) is an evidence-informed mental health and resiliency training programme designed to mitigate the effects of PTSI. We conducted 41 in-depth semi-structured interviews with PSP and HCP who had completed the BOS program to investigate whether and how the programme benefits mental health, and how the content can integrate with their personal and professional lives. Data were analysed using team-based template analysis. The four overarching themes that emerged were: (1) health journeys; (2) the ripple effect of helping; (3) the destabilising effect of organizational pressures; and (4) context matters to how the programme was received. Each main theme was developed and supported by multiple subthemes explored herein. The results indicated BOS was well-received and helpful to participants at various career stages while furthering the discourse on mental health in PSP and HCP workplaces; however, difficulties were reported related to persistent stigma around mental health injuries. Organizational policies and systemic strains appear to be key contextual determinants and barriers.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".