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Record W2024908107 · doi:10.1097/jom.0b013e31814b2e9f

The Impact of Early Workplace-Based Return-to-Work Strategies on Work Absence Duration: A 6-Month Longitudinal Study Following an Occupational Musculoskeletal Injury

2007· article· en· W2024908107 on OpenAlexaff
Renée‐Louise Franche, Colette N. Severin, Sheilah Hogg‐Johnson, Pierre Côté, Marjan Vidmar, Hyunmi Lee

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsInstitute for Work & HealthUniversity of Toronto
Fundersnot available
KeywordsDuration (music)Occupational safety and healthMedicineWork (physics)Proportional hazards modelPsychological interventionHazard ratioMusculoskeletal injuryOccupational injuryCohortOccupational medicineCohort studyPhysical therapyAccommodationHuman factors and ergonomicsPoison controlPsychologyEnvironmental healthNursingConfidence intervalSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine, using administrative and self-reported data, the relationship between early return-to-work (RTW) strategies and work absence duration. METHODS: Using a cohort of 632 claimants with work-related musculoskeletal injuries, Cox proportional hazard analyses were performed with RTW strategies measured 1 month after injury as predictors. Outcomes were 6-month self-reported work absence duration and time receiving wage replacement benefits from an administrative database. RESULTS: Work accommodation offer and acceptance and advice from health care provider (HCP) to the workplace on re-injury prevention were significant predictors of shorter work absence duration indexed by both self-report and administrative data. Receiving an ergonomic visit was a significant predictor of shorter duration receiving benefits only. CONCLUSIONS: Analyses using administrative and self-reported indices of work absence generally converged. Work accommodation and targeted HCP communication with the workplace are critical for effective early RTW interventions.

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)0.000
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 score0.011
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.041
GPT teacher head0.422
Teacher spread0.381 · 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

Citations79
Published2007
Admission routes1
Has abstractyes

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