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Record W3011200522 · doi:10.1097/jom.0000000000001850

Urban-Rural Differences in the Duration of Injury-Related Work Disability in Six Canadian Provinces

2020· article· en· W3011200522 on OpenAlexafffundabout
Robert Macpherson, Benjamin C. Amick, Alex Collie, Sheilah Hogg‐Johnson, Mieke Koehoorn, Peter Smith, Chris McLeod

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & HealthCanadian Memorial Chiropractic CollegeCanadian Institutes of Health ResearchUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsDuration (music)Work (physics)Occupational safety and healthGeographyEnvironmental healthInjury preventionDemographyGerontologyMedicinePoison controlSocioeconomicsSociologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine associations between injury-related work disability duration and urban-rural place of residence and whether associations differed across the disability distribution and by industry sector. METHODS: Workers' compensation claims from six Canadian provinces were extracted between 2011 and 2015. Multivariable quantile regression models tested the associations between urban-rural place of residence and disability days paid between the 50th and 95th percentiles of the distribution. RESULTS: Compared to workers residing in metropolitan areas, those in all other areas experienced more disability days paid. Urban-rural differences increased toward the upper end of disability distribution and were largest in the construction, and transportation and warehousing sectors. CONCLUSION: Tailored interventions for workers in rural areas, particularly those in sectors associated with mobile work environments, may be warranted to reduce inequities in injury-related work disability duration by place of residence.

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)0.002
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 score0.019
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.063
GPT teacher head0.388
Teacher spread0.325 · 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

Citations13
Published2020
Admission routes3
Has abstractyes

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