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Record W2082771750 · doi:10.1002/ajim.20503

Social and economic consequences of workplace injury: A population‐based study of workers in British Columbia, Canada

2007· article· en· W2082771750 on OpenAlexafffundabout
Judy Brown, Harry S. Shannon, Cameron Mustard, Peggy McDonough

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & HealthMcMaster UniversityUniversity of Toronto
FundersU.S. Public Health ServiceWorkSafeBCWorkplace Safety and Insurance Board
KeywordsMedicineOccupational safety and healthLogistic regressionMarital statusInjury preventionHuman factors and ergonomicsPopulationDemographyPoison controlSuicide preventionGerontologyEnvironmental healthWorkers' compensationOccupational injuryOccupational medicineCompensation (psychology)Occupational exposurePsychologySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Existing research suggests that workplace injuries can have significant economic and social consequences for workers; but there are no quantitative studies on complete populations. METHODS: The British Columbia Linked Health Database (BCLHD) was used to examine 1994 injured workers who lost work time due to the injury (LT) and a group of injured individuals who did not lose time after their injuries (NLT). Three outcomes were explored: (1) residential change, (2) marital instability, and (3) social assistance use. Logistic regression adjusted for several individual and injury characteristics. RESULTS: LTs were more likely to move and collect income assistance benefits, and less likely to experience a relationship break-up than the NLTs. LTs off work for 12 or more weeks were more likely to receive income assistance than LTs off for less time. CONCLUSIONS: The increased risk suggests that the long-term economic consequences of disabling work injury may not be fully mitigated by workers compensation benefits.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.053
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.447
Teacher spread0.356 · 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

Citations40
Published2007
Admission routes3
Has abstractyes

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