Labor Force Participation in Adults With Osteoarthritis or Joint Symptoms Typical of Osteoarthritis: Findings From a Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: The purpose of the study is to examine the relationship between osteoarthritis (OA) and joint symptoms typical of OA and labor force participation. METHODS: Data are from the baseline questionnaire of the Canadian Longitudinal Study on Aging for respondents aged 45 to 74 years at baseline (n = 24,427). Individuals were categorized into one of five mutually exclusive arthritis status groups: diagnosed OA, diagnosed other type of arthritis, two to three symptomatic joint sites and no diagnosed arthritis, one symptomatic joint site and no diagnosed arthritis, and no arthritis and no joint symptoms. Age-stratified robust log-Poisson regression analysis was used to examine the association between arthritis status and labor force participation. RESULTS: Overall, 39% of the analytic sample reported being out of the labor force. Those with OA aged 45 to 54 and 55 to 64 years were significantly more likely to be out of the labor force than those with no arthritis or no joint symptoms, with prevalence ratios (PRs) of 1.34 (95% confidence interval [CI] 1.10-1.65) and 1.13 (95% CI 1.06-1.21), respectively, with similar results for those with two to three joint symptoms and no OA in the 45 to 54 years age group (PR 1.37 [95% CI 1.07-1.76]). There was no difference for those aged 65 to 74 years. Being an informal caregiver increased the likelihood of nonparticipation in the labor force for those aged 55 to 64 years (PR 1.09 [95% CI 1.04-1.15]). CONCLUSION: Our results suggest that an exclusive reliance on an OA diagnosis to understand impact on labor force participation may miss a large segment of the middle-aged population, which may have undiagnosed OA or be at greater risk of OA because of joint problems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".