Reexamining the arthritis‐employment interface: Perceptions of arthritis‐work spillover among employed adults
Bibliographic record
Abstract
OBJECTIVE: To examine employed individuals' perceptions of arthritis-work spillover (AWS), the reciprocal influence of arthritis on work and work on arthritis, and the demographic, illness, and work context factors associated with AWS. METHODS: The study group comprised 492 employed individuals with osteoarthritis or inflammatory arthritis. Participants completed an interview-administered, structured questionnaire assessing AWS, demographic (e.g., age, sex), illness (e.g., disease type, pain, activity limitations), and work context (e.g., workplace control, hours of work) variables. Principal components analysis, reliability analysis, and multiple linear regression were used to analyze the data. RESULTS: A single factor solution emerged for AWS. The scale had an internal reliability of 0.88. Respondents were more likely to report that work interfered with caring for their arthritis than they were to report that their disease affected their work performance. Younger respondents, those with more fatigue and workplace activity limitations, and those working in trades and transportation reported more AWS. Individuals with more control over their work schedules reported less AWS. CONCLUSION: The results of this study extend research on arthritis by reexamining the interface between arthritis and employment. This study introduces a new measure of AWS that enhances the range of tools available to researchers and clinicians examining the impact of arthritis in individuals' lives.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".