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Record W4298087677 · doi:10.1177/00469580221125765

Psychological, Physical and Behavioral Health of Adults, 3 Years After Exposure to a Train Derailment

2022· article· en· W4298087677 on OpenAlexafffund
Danielle Maltais, Mélissa Généreux, Mathieu Roy, Geneviève Fortin, Ève Pouliot, Christiane Bergeron‐Leclerc, Jacques Cherblanc, Óscar Labra, Lise Lachance, Linda Paquette

Bibliographic record

VenueINQUIRY The Journal of Health Care Organization Provision and Financing · 2022
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité de SherbrookeUniversité du Québec à Chicoutimi
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDerailmentLogistic regressionOddsPopulationMedicineDemographyGerontologyEnvironmental healthPsychologyGeography

Abstract

fetched live from OpenAlex

In July 2013, a train derailment profoundly disrupted the tranquility of the population of Lac-Mégantic for months and even years. In 2016, we conducted a representative population-based survey among 387 people from Lac-Mégantic and 413 from other municipalities with the aim to document psychological and physical health of adults exposed to the disaster. This article examines differences between 3 groups of respondents: those who were highly, moderately or not exposed to the train accident. Khi Square analyses, odds ratios and logistic regressions were used to examine differences between the 3 groups of respondents (high, moderate and no exposure). Results show that the level of exposure to this technological disaster is strongly associated with psychological suffering, post-traumatic growth, physical heath, drinking patterns, and use of prescribed and non-prescribed drugs. We can explain these results by the nature and cause of the event as well as its consequences.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.409
Teacher spread0.361 · 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 teacher head, 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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueINQUIRY The Journal of Health Care Organization Provision and FinancingSame topicPosttraumatic Stress Disorder ResearchFrench-language works237,207