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Record W2134769783 · doi:10.1097/jom.0b013e31828dc9ea

Nonwage Losses Associated With Occupational Injury Among Health Care Workers

2013· article· en· W2134769783 on OpenAlexaffabout
Jaime Guzmán, Aybaniz Ibrahimova, Emile Tompa, Mieke Koehoorn, Hasanat Alamgir

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOccupational injuryOccupational safety and healthMedicineConfidence intervalCohortQuality of life (healthcare)Environmental healthInjury preventionDemographyPoison controlNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine nonwage losses after occupational injury among health care workers and the factors associated with the magnitude of these losses. METHODS: Inception cohort of workers filing an occupational injury claim in a Canadian province. Worker self-reports were used to calculate (1) the nonwage economic losses in 2010 Canadian dollars, and (2) the number of quality-adjusted days of life lost on the basis of the EuroQOL Index. RESULTS: Most workers (84%; n = 123) had musculoskeletal injuries (MSIs). Each MSI resulted in nonwage economic losses of Can$3131 (95% confidence interval, Can$3035 to Can$3226), lost wages of Can$5286, and 7.9 quality-adjusted days of life lost within 12 weeks after injury. Losses varied with type of injury, region of the province, and occupation. Non-MSIs were associated with smaller losses. CONCLUSIONS: These estimates of nonwage losses should be considered in workers' injury compensation policies and in economic evaluation studies.

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)0.006
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 score0.312
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.413
Teacher spread0.360 · 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".

Quick stats

Citations2
Published2013
Admission routes2
Has abstractyes

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