Two years after the train derailment: Lac-Megantic (Quebec, Canada) residents are still suffering
Bibliographic record
Abstract
Background In 2013, a train carrying 72 cars of oil derailed in Lac-Mégantic, the seat of the Granit territory, in the Estrie region. The explosions and the raging fire killed 47 people, destroyed much of the downtown area, and heavily contaminated the environment. The health consequences were examined in the years following the disaster. Methods Two phone surveys were conducted, one in 2014 and another in 2015, among random samples of adults residing in the Estrie region (2014: n = 8737; 2015: n = 1600). Using Chi-square and t-tests, the frequency of physical and mental health issues was compared according to residential location (Lac-Mégantic, Granit, Estrie) and over time (2014 and 2015). Results Overall, 7 in 10 adults in Granit reported human (e.g. loss of a loved one) or material losses (e.g. home damage). In 2015, 19.3% of adults in Lac-Mégantic did not consider themselves healthy, a proportion twice as high as elsewhere in the Estrie region (9.6%, p < 0.001). This proportion was higher than the one measured in 2014 in Lac-Mégantic (13.0%; p = 0.03). In 2015, anxiety disorders were twice as common in Lac-Mégantic as elsewhere in the region (14.1% vs. 7.2%, p = 0.03). Similar findings were observed for psychological distress (34.1% vs. 22.1%, p < 0.001). No improvement was noted over time for these issues. Despite the significant proportion of people affected by the tragedy, visits to psychologists and social workers decreased by half since 2014 (15.5% vs. 26.9%, p = 0.001). Conclusions In the Granit, particularly in Lac-Mégantic, health and social problems are persistent and even increasing 2 years after the tragic event, while the consultation for psychosocial aid has declined. Secondary stress factors may increase the sense of distress in individuals and affect its duration. To increase resilience in the coming years, the local health network needs to maintain resources, adapt psychosocial services, stay connected with the community, and foster resident involvement. Key messages: The population burden of psychopathology in the aftermath of disasters is substantial and may span several years This calls for sustained effort from everyone and requires a flexible approach
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".