The public health response during and after the Lac-Mégantic train derailment disaster
Bibliographic record
Abstract
Issue/problem On July 6th 2013, a train carrying 72 cars of oil derailed in Lac-Mégantic, Quebec, Canada, causing major human, economic and environmental impacts. In response to this unprecedented disaster, the Estrie Public Health Department (EPHD) initially focused on acute consequences (mostly health risks associated with environmental contamination) and on emergency response operations. Three years after the event, EPHD is still involved, supporting the social and psychological recovery of the community. Description of the problem Public health organizations need to be prepared to deal with complex disasters. In this case study, we comprehensively describe and analyze actions taken by the EPHD during both the emergency response and the recovery operations phases. Results Due to the complexity of the event, public health actions needed to be diversified. These actions targeted chemical (e.g. toxic cloud), physical (e.g. heat wave), biological (e.g. water contamination) and psychosocial (e.g. stress) hazards. Actions initially undertaken were: risk assessment, evacuation and reintegration, coordination with multi-sectoral partners, epidemiological investigation, and risk communication. In the months and years following the disaster, EPHD undertook many actions with different community partners to support the psychological recovery and the resilience process: health surveillance, research, community development, occupational health (including workplace psychosocial interventions), and health impact assessment (related to the downtown reconstruction and bypass train route). Lessons Our analyses yielded seven lessons that will improve and inform response to future events. The most important lesson of all is that people should never underestimate the long-term impacts of a tragedy, especially on mental health and psychological well-being. Our lessons could serve as a basis to develop a conceptual framework for public health emergency preparedness. Key messages: Our analysis of the public health response in Lac-Mégantic illustrates the broad spectrum of actions required These actions ensure that the short- and long-term impacts of such a disaster are minimized
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".