Differences in the workforce experiences of women and men with arthritis disability: A population health perspective
Bibliographic record
Abstract
OBJECTIVE: To examine the employment status characteristics of people with arthritis disability, with a focus on gender differences and who remains in the workforce. METHODS: Analyses were based on cross-sectional, self-reported data of the Canadian Participation and Activity Limitation Survey, administered in 2001-2002 (n = 28,908). Labor force status was categorized into employed, unemployed, and not in the labor force. Prevalence estimates were derived from descriptive analyses, and logistic regression determined the factors associated with being out of the labor force. Chi-square and sex-stratified analyses examined gender differences. RESULTS: An estimated 2.3% of the working-age population (ages 25-64 years) reported arthritis disability, and >50% were out of the labor force. Being female, single, older, and having less education and more severe pain and disability were associated with being out of the labor force. Employed women with arthritis disability required more accommodations in the workplace and reported more activity limitations than men. Perceived discrimination was more likely to be reported by employed men, and men reported more changes to their work than women. CONCLUSION: This study underscores the importance of looking more closely at differences in the employment experiences of women and men. Specifically, the results suggest that arthritis may marginalize women and men in different ways. Women may be more likely to leave employment, whereas men may be more likely to remain working and report negative workplace experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".