Posttraumatic Stress Disorder, Trauma Exposure, and the Current Health of Canadian Bus Drivers
Bibliographic record
Abstract
Objective: Pre vi ous stud ies of vet er ans have linked posttraumatic stress dis or der (PTSD) af ter com bat-related trauma to increased re ports of health prob lems. It is un clear whether this as so ci a tion be tween PTSD and in creased health prob lems gen er a lizes to ci vil ians who are ex posed to a broader ar ray of trau matic events. We also do not know whether trauma ex po sure i s as so ci ated with in creased health prob lems in in di vid u als who do not de velop PTSD. Using a non–treat ment-seeking ci vil ian sample, we ex am ined whether life time PTSD or trauma ex po sure by it self was as so ci ated with c ur rent health prob lems. Methods: Using a cross-sectional de sign and self-report mea sures, we eval u ated ur ban Ca na dian bus driv ers (n = 342) on trauma ex po sure, life time PTSD, and cur rent health prob lems. Based on their re sponses, we di vided o ur sam ple into in di vid u als who had never ex pe ri enced trauma (n = 91), trauma-exposed in di vid u als who had never de vel oped PTSD (n = 218), and per sons who devel oped PTSD at some point af ter trauma (n = 33). We com pared these groups on health prob lems, treat ment ser vice use, and health as sess ment mea sures. Results: The PTSD group re ported i n creased health c o m plaints, more fre quent use of health treat ments, and poorer health self-ratings com pared with the ex posed non-PTSD and nonexposed groups. Trauma-exposed driv ers wi th out PTSD did not dif fer from un ex posed driv ers on any health mea sure. Con trolling for sex and trauma fre quency did not al ter our find ings. Conclusions: Trauma ex po sure that leads to PTSD is as so ci ated with in creased health prob lems, while trauma ex po sure alone is not. Our re sults ex tend pre vi ous find ings to a broader ci vil ian con text and clar ify as soci a tions be tween trauma ex po sure and health.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".