Methodological correlates of variability in the prevalence of posttraumatic stress disorder in high‐risk occupational groups: A systematic review and meta‐regression
Bibliographic record
Abstract
BACKGROUND: Although numerous studies have reported on PTSD prevalence in high-risk occupational samples, previous meta-analytic work has been severely limited by the extreme variability in prevalence outcomes. METHODS: The present systematic review and meta-regression examined methodological sources of variability in PTSD outcomes across the literature on high-risk personnel with a specific focus on measurement tool selection. RESULTS: The pooled global prevalence of PTSD in high-risk personnel was 12.1% [6.5%, 23.5%], and was similar to estimates obtained in other meta-analytic work. However, meta-regression revealed that PTSD prevalence differed significantly as a function of measurement tool selection, study inclusion criteria related to previous traumatic exposure, sample size, and study quality. PTSD prevalence estimates also differed significantly by occupational group and over time, as has also been reported in previous work, though exploratory examination of trends in measurement selection across these factors suggests that measurement strategy may partially explain some of these previously reported differences. CONCLUSIONS: Our results highlight a pressing need to better understand the role of measurement strategies and other methodological choices in characterizing variable prevalence outcomes. Understanding the role of methodological variance will be critical for work attempting to reliably characterize prevalence as well as risk and protective factors for PTSD.
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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 | 0.047 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".