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Record W3162043782 · doi:10.1097/jom.0000000000002262

Evaluation of the Implementation of a Railway Critical Incident Management and Support Protocol to Help Train Drivers Cope With Accidents and Suicides

2021· article· en· W3162043782 on OpenAlexaff
Cécile Bardon, Luc Dargis, Brian L. Mishara

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2021
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversité du Québec à MontréalDefence Research and Development Canada
Fundersnot available
KeywordsIncident managementHuman factors and ergonomicsProtocol (science)Occupational safety and healthWork (physics)Poison controlInjury preventionIncident reportSuicide preventionApplied psychologyTransport engineeringCritical Incident TechniquePsychologyComputer scienceMedical emergencyMedicineComputer securityEngineeringBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: Railway accidents and suicides can have severe psychological consequences for train drivers. This study evaluates the implementation of railway critical incident management and support protocols (CIMSP) by employers. It also identifies environmental factors, characteristics of critical incidents, and types of work relations affecting implementation. METHODS: A longitudinal study was conducted with 74 train drivers. Participants were interviewed 1 week, 1, 3, and 6 months after a critical incident. Correlational analyses were performed to identify factors associated with implementation and satisfaction. RESULTS: CIMSP are generally partially applied by employers when a railway incident occurs. Workers' satisfaction toward implementation of the protocol is moderate. Obstacles to implementation are: geographic isolation, severity of the incident, and poor quality of work relations. CONCLUSIONS: These obstacles should be addressed in CIMSP design and implementation strategies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.997

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)0.0040.000

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 head0.047
GPT teacher head0.435
Teacher spread0.389 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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