Best practice intervention for post-traumatic stress disorder among transit workers
Bibliographic record
Abstract
BACKGROUND: Transportation industry workers are at high risk for exposure to traumatic incidents in the workplace. A considerable number of those exposed to such incidents will develop post-traumatic stress disorder (PTSD) symptoms, which leads to high rates of absenteeism and are costly to the public transit corporation and workplace safety compensation insurance. OBJECTIVE: To determine whether the newly implemented Best Practice Intervention (BPI) provides superior outcomeswhen compared with Treatment-as-Usual (TAU) interventions in improving workers' rates of return to work (RTW), decreasing duration of time lost from work and overall reduction in severity of PTSD symptoms 6 months after exposure. METHODS: A sequential mixed methods approach was used with qualitative analysis followed by a pre-post intervention design. Sixty-two participants were recruited to the (TAU) phase of the study and 79 to the (BPI) phase. RESULTS: Significant differences were observed between the TAU and BPI groups in number of lost work days (TAU: 20 days vs. BPI: 52 days, p = 0.02). PTSD symptoms decreased with time (MPPS score: 51.3 vs. 24.35; p < 0.001). One-fifth of the participants (21 %) did not return to work by the end of the 6 months follow-up period. CONCLUSIONS: The study demonstrated the value of workplace interventions in improving awareness of psychological symptoms after exposure to a traumatic incident and the value of screening for PTSD symptoms.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".