Help-seeking in transit workers exposed to acute psychological trauma: A qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Traumatic events often occur in workplace settings and can lead to stress reactions such as Post-Traumatic Stress Disorder (PTSD). One such workplace is the transportation industry, where employees are often exposed to trauma. However, extant research shows that a considerable proportion of people with PTSD do not seek specialty mental health treatment. OBJECTIVE: In this qualitative study, we sought to better understand the experience of a traumatic event at work and the barriers and motivating factors for seeking mental health treatment. PARTICIPANTS: Twenty-nine Toronto Transit Commission (TTC) employees participated in a one-on-one interview, 18 soon after the traumatic event and 11 after entering a specialized treatment program. METHODS: Semi-structured, one-on-one interviews were conducting using qualitative description and analyzed using content analysis. RESULTS: Participants described emotional responses after the trauma such as guilt, anger, disbelief as particularly difficult, and explained that barriers to seeking help included the overwhelming amount and timing of paperwork related to the incident as well as negative interactions with management. Motivating factors included family and peer support, as well as financial and emotional issues which persuaded some to seek help. CONCLUSIONS: Seeking treatment is a multifactorial process. Implications and recommendations for the organization are discussed.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".