Dangers on the road: A longitudinal examination of passenger‐initiated violence against bus drivers
Bibliographic record
Abstract
This study examined the impact of workplace violence against 109 bus drivers over a 1-year span. Workplace violence is related to both psychological and work-related consequences. Our findings showed that bus drivers experienced a wide range of violence at work and the psychological consequences were devastating: Half of the participants met the diagnostic criteria for acute stress disorder within the first month following the index event. Majority of them experienced at least moderate levels of post-traumatic stress disorder (PTSD) problems over the 1-year span. About 9.3% of participants showed a delayed onset of PTSD 6 months after. Furthermore, counter-supportive behaviours and reexposure to violence played important roles in the maintenance of PTSD symptoms over time. Even though PTSD symptoms per se did not relate to bus driver's confidence in coping with aggressive passengers, the immediate post-traumatic reaction-symptoms of acute stress disorder-showed a significant long-term negative effect on bus drivers' confidence in dealing with aggressive passengers 12 months after. This study provided empirical evidence of the changing nature of PTSD symptoms over time among bus drivers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".