Mental health disorder symptom changes among public safety personnel after emotional resilience skills training
Bibliographic record
Abstract
OBJECTIVES: Public safety personnel (PSP) are frequently exposed to psychologically traumatic events. The exposures potentiate posttraumatic stress injuries (PTSIs), including posttraumatic stress disorder (PTSD). The Royal Canadian Mounted Police (RCMP) Protocol was designed to mitigate PTSIs using ongoing monitoring and PSP-delivered Emotional Resilience Skills Training (ERST) based on the Unified Protocol for the Transdiagnostic Treatment of Emotional Disorders. The current study pilot-tested ERST effectiveness among diverse PSP. METHODS: A 16-month longitudinal design engaged serving PSP (n = 119; 34 % female; firefighters, municipal police, paramedics, public safety communicators) who completed PSP-delivered ERST. Participants were assessed for symptoms of PTSIs, including but not limited to PTSD, at pre- and post-training, and 1-year follow-up using self-report measures and clinical interviews. RESULTS: There were reductions in self-report and clinical diagnostic interview positive screens for PTSD and other PTSI from pre- to post-training (ps < 0.05), with mental health sustained or improved at 1-year follow-up. Improvements were observed among firefighters (Cohen's d = 0.40 to 0.71), police (Cohen's d = 0.28 to 0.38), paramedics (Cohen's d = 0.20 to 0.56), and communicators (Cohen's d = 0.05 to 0.14). CONCLUSION: Ongoing monitoring and PSP-delivered ERST, can produce small to large mental health improvements among diverse PSP, or mitigate PSP mental health challenges, with variations influenced by pre-training factors and organizational supports. ERST replication and extension research appears warranted. TRIAL REGISTRATION: Hypotheses Registration: aspredicted.org, #90136. Registered 7 March 2022 - Prospectively registered. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05530642.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".