Arthritis‐related work transitions: A prospective analysis of reported productivity losses, work changes, and leaving the labor force
Bibliographic record
Abstract
OBJECTIVE: To prospectively examine arthritis-related productivity losses, work changes, and leaving employment, the relationships among these work transitions, and the factors associated with them. METHODS: Participants with inflammatory arthritis or osteoarthritis were interviewed at 4 time points, 18 months apart, using a structured questionnaire. At baseline (T1), all participants (n = 490; 381 women, 109 men) were employed. At T2, T3, and T4, the sample decreased to 413, 372, and 349 participants, respectively. Respondents were recruited using community advertising and from rheumatology and rehabilitation clinics. Work transitions considered were productivity losses (absenteeism, job disruptions), work changes (reduced hours, changing jobs), and leaving employment. Also measured were demographic, illness, work context, and psychological variables. Generalized estimation equations modeled predictors of work transitions over time. RESULTS: Although 63.1% of respondents remained employed throughout the study, work transitions were common (reported by 76.5% of participants). Productivity losses, especially job disruptions such as being unable to take on extra work, were the most frequently reported. Work transitions were related to subsequently making other work transitions, including leaving employment. Age, sex, education, activity limitations, control, depression, and arthritis-work spillover were also associated with work transitions. CONCLUSION: This study sheds light on a process of diverse employment changes that may occur in the lives of many individuals with arthritis. It emphasizes the interrelationships among work transitions, as well as other factors in predicting work transitions, and it provides insight into work changes that may signal impending difficulties with remaining employed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".