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Record W2946903110 · doi:10.1017/s1049023x19004321

Monitoring Adverse Psychosocial Outcomes One and Two Years After the Lac-Mégantic Train Derailment Tragedy (Eastern Townships, Quebec, Canada)

2019· article· en· W2946903110 on OpenAlexaffabout
Mélissa Généreux, Danielle Maltais, Geneviève Petit, Mathieu Roy

Bibliographic record

VenuePrehospital and Disaster Medicine · 2019
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversité du Québec à ChicoutimiCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsPsychosocialPopulationEnvironmental healthMedicineAnxietyPsychological interventionDistressDemographyPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: In July 2013, a train carrying 72 cars of crude oil derailed in the town of Lac-Mégantic (Eastern Townships, Quebec, Canada). This disaster provoked a major conflagration, explosions, 47 deaths, the destruction of 44 buildings, the evacuation of one-third of the local population, and an unparalleled oil spill. Notwithstanding the environmental impact, many citizens of this town and in surrounding areas have suffered and continue to suffer substantial losses as a direct consequence of this catastrophe. PROBLEM: To tailor public health interventions and to meet the psychosocial needs of the community, the Public Health Department of Eastern Townships has undertaken repeated surveys to monitor health and well-being over time. This study focuses on negative psychosocial outcomes one and two years after the tragedy. METHODS: Two cross-sectional surveys (2014 and 2015) were conducted among large random samples of adults in Lac-Mégantic and surrounding areas (2014: n = 811; 2015: n = 800), and elsewhere in the region (2014: n = 7,926; 2015: n = 800). A wide range of psychosocial outcomes was assessed (ie, daily stress, main source of stress, sense of insecurity, psychological distress, excessive drinking, anxiety or mood disorders, psychosocial services use, anxiolytic drug use, gambling habits, and posttraumatic stress symptoms [PSS]). Exposure to the tragedy was assessed using residential location (ie, six-digit postal code) and intensity of exposure (ie, intense, moderate, or low exposure; from nine items capturing human, material, or subjective losses). Relationships between such exposures and adverse psychosocial outcomes were examined using chi-squares and t-tests. Distribution of outcomes was also examined over time. RESULTS: One year after the disaster, an important proportion of participants reported human, material, and subjective losses (64%, 23%, and 54%, respectively), whereas 17% of people experienced intense exposure. Participants from Lac-Mégantic, particularly those intensely exposed, were much more likely to report psychological distress, depressive episode, anxiety disorders, and anxiolytic drug use, relative to less-exposed ones. In 2015, 67% of the Lac-Mégantic participants (76% of intensely exposed) reported moderate to severe PSS. Surprisingly, the use of psychosocial services in Lac-Mégantic declined by 41% from 2014 to 2015. CONCLUSION: The psychosocial burden in the aftermath of the Lac-Mégantic tragedy is substantial and persistent. Public health organizations responding to large-scaling disasters should monitor long-term psychosocial consequences and advocate for community-based psychosocial support in order to help citizens in their recovery process.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.319
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations12
Published2019
Admission routes2
Has abstractyes

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