Evaluation of Before Operational Stress: A Program to Support Mental Health and Proactive Psychological Protection in Public Safety Personnel
Bibliographic record
Abstract
Public safety personnel (PSP; e.g., communications officials, corrections workers, firefighters, paramedics, and police officers) are at risk of developing mental health problems due to experiencing potentially psychologically traumatic events during their career. Research examining evidence-based treatments for psychological injuries resulting from operational duties (also known as operational stress injuries) has not yielded robust results that would indicate ongoing interventions as the best solution for managing PSP mental health injuries; as such, proactive psychological interventions designed to bolster resilience are being considered potentially beneficial for mitigating the impact of occupational stress on PSP. Despite the growing popularity of resilience programs, most are delivered in a single session after an event deemed particularly problematic with no follow-up. Longer interventions may better support sustained resiliency, mitigate the impact of operational stress, and increase positive PSP workplace outcomes. The current article introduces the Before Operational Stress (BOS) program, which was designed for delivery early in a PSP career to enhance self-awareness and healthy relationships. The year-long program is derived from cognitive behavior therapy and group therapeutic techniques to meet program objectives. The current BOS program evaluation demonstrated small, statistically significant improvements in symptoms of PTSD, quality of life, stigma, and perceived social support from baseline (Time 1) to 6 months (Time 4). There were also non-significant improvements observed in symptoms of depression, anxiety, stress, alcohol use, as well as in emotional regulation and resilience. Qualitative results indicated participants positively perceived the BOS program, with participants reporting specific improvements in self-awareness, avoidant behaviors, and relationships with family and colleagues. The BOS program content (e.g., functional disconnection and functional reconnection) and processes (e.g., psychoeducation within a supportive learning structure; mutually empowering group interactions) appear unique relative to other PSP resilience programs, with promising initial results in support of PSP mental health. Recommendations for future research and program development are provided.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".