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Record W2774597715 · doi:10.1136/oemed-2017-104636.386

0465 Gender, age, and the changing burden of work-related disability in canada and australia

2017· article· en· W2774597715 on OpenAlexaffabout
Robert Macpherson, Tyler Lane, Alex Collie, Mieke Koehoorn, Peter Smith, Benjamin C. Amick, Sheilah Hogg‐Johnson, Chris McLeod

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsInstitute for Work & HealthUniversity of British Columbia
Fundersnot available
KeywordsWork (physics)DemographyMedicineGerontologySociologyEngineering

Abstract

fetched live from OpenAlex

Objectives This research investigates the changing burden of work-related disability in Canada and Australia and how this varies by gender and age. The secondary objective is to demonstrate a means of comparing work disability data internationally. Methods Workers’ compensation data from Canada and Australia were used to analyse the relative disability burden of workers injured between 2004 and 2010. The two measures used were the number of claims with compensated time-loss and the corresponding time-loss years accrued, indexed to 2004. Gender and age-stratified analyses were conducted using descriptive statistics. Results Male workers had more claims and cumulative time-loss in both countries. They also had steeper reductions in claim volumes and cumulative time-loss over time, indicating a narrowing in overall gender differences. Age-stratified analysis showed that differences between men and women were smaller among younger workers compared to older workers. In Canada, the proportion of claims attributable to females grew at the same rate as the proportion of time loss until 2007–08 when a gap emerged. In Australia, the proportion of claims and time loss attributable to females grew closer over time. Conclusions While the volume of claims and cumulative time-loss has decreased in Canada and Australia, and the largest proportion is attributable to workers who are male and aged 35–54, a growing proportion is attributable to female and older workers. These changes have been driven by demographic factors (growth of females in the workforce, ageing workforce) and structural factors (economic recession and policy changes), particularly in Canada.

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)0.004
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 score0.026
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.212
GPT teacher head0.402
Teacher spread0.190 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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