Putative risk and resiliency factors after an augmented training program for preventing posttraumatic stress injuries among public safety personnel from diverse sectors
Bibliographic record
Abstract
Mental health disorders are particularly prevalent among public safety personnel (PSP). Emotional Resilience Skills Training (ERST) is a cognitive behavioural training program for PSP based on the Unified Protocol for the Transdiagnostic Treatment of Emotional Disorders (i.e. Unified Protocol). The current study was designed to assess whether ERST is associated with reduced putative risk factors for mental disorders and increased individual resilience. The PSP-PTSI Study used a longitudinal prospective sequential experimental cohort design that engaged each participant for approximately 16 months. PSP from diverse sectors (i.e. firefighters, municipal police, paramedics, public safety communicators) completed self-report measures of several putative risk variables (i.e. anxiety sensitivity, fear of negative evaluation, pain anxiety, illness and injury sensitivity, intolerance of uncertainty, state anger) and resilience at three time points: pre-training (n = 191), post-training (n = 103), and 1-year follow-up (n = 41). Participant scores were statistically compared across time points. Participants reported statistically significantly lower scores on all putative risk variables except pain anxiety, and statistically significantly higher resilience from pre- to post-training. Changes were sustained at 1-year follow-up. The results indicate that ERST is associated with reductions in several putative risk variables and improvement in resilience among PSP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".