Seniors Who Experienced the Lac-Mégantic Train Derailment Tragedy: What Are the Consequences on Physical and Mental Health?
Bibliographic record
Abstract
Introduction: In July 2013, a train derailment caused the death of 47 people and destroyed the downtown area in the city of Lac-Mégantic (Quebec, Canada). This tragedy had several impacts on this small community. Method: Three years after this disaster, we used a representative population-based survey conducted among 800 adults (including 265 seniors aged 65 or above) to assess the physical and mental health of seniors. Results: Several differences were observed in seniors’ physical and mental health based on their level of exposure to the tragedy. Nearly half of seniors highly exposed to the train derailment (41.7%) believe that their health has deteriorated in the past 3 years. The majority of seniors highly exposed to the train derailment (68.7%) also show symptoms of posttraumatic stress disorders. Seniors highly or moderately exposed to the tragedy were also more likely to have found positive changes in their personal and social life as compared with nonexposed seniors. Discussion: A technological disaster such as a train derailment still had negative impacts on seniors’ physical and mental health 3 years later. Conclusion: Public health authorities must tailor prevention and promotion programs to restore health and well-being in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".