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Record W3087312249 · doi:10.1097/jom.0000000000002029

Age Differences in Return-to-Work Following Injury

2020· article· en· W3087312249 on OpenAlexaff
Jonathan Fan, Monique A. M. Gignac, M. Anne Harris, Peter Smith

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & HealthPublic Health Ontario
Fundersnot available
KeywordsPsychosocialDemographyGerontologyCohortAssociation (psychology)Construct (python library)PsychologyAge groupsCohort studyMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the overall association between chronological age and return-to-work (RTW), and understand if existing data could be used to better understand the role of age-related dimensions (functional, psychosocial, organizational, life-stage) in explaining these associations. METHODS: We used survey data from a prospective cohort of injured workers in Victoria, Australia. Path models examined the relationship between chronological age and RTW, and the proportion mediated via age dimensions. RESULTS: Older chronological age was associated with non-RTW, although the pattern was not observed consistently across follow-up surveys. A proportion of the overall relationship between chronological age and non-RTW was explained by functional and life-stage age and RTW status at previous time points. CONCLUSIONS: Findings underscore the importance of moving beyond age measured only in chronological years, towards more complex conceptual and analytical models that recognize age as a multidimensional construct.

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)0.007
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.124
GPT teacher head0.440
Teacher spread0.316 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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