Occupational exposure and post-traumatic stress disorder: A rapid review
Bibliographic record
Abstract
BACKGROUND: Post-Traumatic Stress Disorder (PTSD) can result from occupational exposures and poses a considerable burden to workers, their families, workplaces and to society in general. OBJECTIVE: Our objective was to conduct a rapid review of the literature to answer the question: "Which occupations have exposures that may lead to a PTSD diagnosis?" METHODS: A rapid review was conducted in six steps: review question development, literature search, study selection (inclusion/exclusion), study characterization, data extraction, and data synthesis. RESULTS: The search identified 3428 unique references which were reviewed to find 16 relevant studies in 23 articles. The articles revealed associations between PTSD and rescue workers (police, firefighters, etc.), health care professionals, transit drivers, and bank employees which seem well supported by the literature. Some studies also suggest potential associations with PTSD and construction and extraction, electricians, manufacturing, installation, maintenance and repair, transportation and material moving, and clerical workers. CONCLUSIONS: A rapid review of the peer-reviewed scientific literature of PTSD prevalence or treatment suggests many occupations have exposures that could be associated with PTSD. Occupational traumatic events were most often associated with PTSD diagnosis. More research is needed to better understand the association between occupation and PTSD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".