Evaluating the Before Operational Stress on-demand asynchronous online training for public safety and healthcare personnel
Bibliographic record
Abstract
Background Public safety personnel (PSP) experience frequent exposures to potentially psychologically traumatic events, increasing their likelihood of developing several mental health disorders. The Before Operational Stress (BOS) program was designed as a proactive psychological intervention to build resilience and improve interpersonal relationships among Canadian PSP. Previous mixed-methods evaluations of the BOS program evidenced small but statistically significant improvements associated with BOS Intensive (in-person) training. A new delivery modality was developed to provide asynchronous online access to program content (i.e., BOS On-Demand) to improve accessibility. Objective The current study was designed to assess the impact of BOS On-Demand with data from a large sample of PSP ( n = 9295; n = 636 [56.1% female] completed all surveys). Methods Participants were administered a self-report survey at pre-training, post-training, and at a 3-month follow-up. Multilevel modeling was used to assess differences in outcome measure changes across timepoints. Results BOS On Demand was associated with several small, but statistically significant, improvements sustained at follow-up, including decreased stress (post-training , p ≤ .001, Cohen’s d = -0.15; follow-up, p ≤ .01, Cohen’s d = -0.19), as well as increased mental health knowledge (pre-training, p ≤ .01, Cohen’s d = 0.13; follow-up, p ≤ .001, Cohen’s d = 0.34). Conclusion The current study provides the first evaluation of the BOS On-Demand program, evidencing encouraging improvements across several measures of 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".