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Record W2168534796 · doi:10.1097/jom.0000000000000551

Health and Occupational Outcomes Among Injured, Nonstandard Shift Workers

2015· article· en· W2168534796 on OpenAlexafffund
Imelda S. Wong, Peter Smith, Cameron Mustard, Monique A. M. Gignac

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2015
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsUniversity of TorontoInstitute for Work & Health
FundersCanadian Institutes of Health Research
KeywordsShift workMedicineRespondentOccupational safety and healthOccupational injuryScheduleCohort studyEnvironmental healthCohortInjury preventionPhysical therapyGerontologyPoison controlDemographyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This study compares health and occupational outcomes following a work-related injury for nonstandard and day-shift workers. METHODS: National Population Health Survey data were used to explore outcomes 2 years post-work injury. Retrospective-matched cohort analyses examined main effects and interactions of shift schedule and work injury with changes in health, shift schedule, and labor force status. Models were adjusted for respondent characteristics, baseline health status, and occupational strength requirements. RESULTS: Injured nonstandard shift workers reported lower health utility index scores, compared with uninjured and injured daytime workers and uninjured nonstandard-shift workers. No significant interactions between shift and injury were found with schedule change and leaving the labor force. CONCLUSIONS: Injured nonstandard-shift workers are as likely to remain employed as other groups, but may be vulnerable in terms of diminished health.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.437

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 head0.045
GPT teacher head0.348
Teacher spread0.303 · 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
Published2015
Admission routes2
Has abstractyes

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