Posttraumatic Stress Disorder, Trauma Exposure, and the Current Health of Canadian Bus Drivers
Bibliographic record
Abstract
OBJECTIVE: Previous studies of veterans have linked posttraumatic stress disorder (PTSD) after combat-related trauma to increased reports of health problems. It is unclear whether this association between PTSD and increased health problems generalizes to civilians who are exposed to a broader array of traumatic events. We also do not know whether trauma exposure is associated with increased health problems in individuals who do not develop PTSD. Using a non-treatment-seeking civilian sample, we examined whether lifetime PTSD or trauma exposure by itself was associated with current health problems. METHODS: Using a cross-sectional design and self-report measures, we evaluated urban Canadian bus drivers (n = 342) on trauma exposure, lifetime PTSD, and current health problems. Based on their responses, we divided our sample into individuals who had never experienced trauma (n = 91), trauma-exposed individuals who had never developed PTSD (n = 218), and persons who developed PTSD at some point after trauma (n = 33). We compared these groups on health problems, treatment service use, and health assessment measures. RESULTS: The PTSD group reported increased health complaints, more frequent use of health treatments, and poorer health self-ratings compared with the exposed non-PTSD and nonexposed groups. Trauma-exposed drivers without PTSD did not differ from unexposed drivers on any health measure. Controlling for sex and trauma frequency did not alter our findings. CONCLUSIONS: Trauma exposure that leads to PTSD is associated with increased health problems, while trauma exposure alone is not. Our results extend previous findings to a broader civilian context and clarify associations between trauma exposure and health.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".