Arthritis symptoms, the work environment, and the future: measuring perceived job strain among employed persons with arthritis
Bibliographic record
Abstract
OBJECTIVE: To develop a measure of job strain related to differing aspects of working with arthritis and to examine the demographic, illness, work context, and psychosocial variables associated with it. METHODS: Study participants were 292 employed individuals with osteoarthritis or inflammatory arthritis. Participants were from wave 3 of a 4-wave longitudinal study examining coping and adaptation efforts used to remain employed. Participants completed an interview-administered structured questionnaire, including a Chronic Illness Job Strain Scale (CIJSS) and questions on demographic (e.g., age, sex), illness and disability (e.g., disease type, pain, activity limitations), work context (e.g., job type, job control), and psychosocial variables (e.g., arthritis-work spillover, coworker/managerial support, job perceptions). Principal component analysis and multiple linear regression were used to analyze the data. RESULTS: A single factor solution emerged for the CIJSS. The scale had an internal reliability of 0.95. Greater job strain was reported for future uncertainty, balancing multiple roles, and difficulties accepting the disease than for current workplace conditions. Participants with inflammatory arthritis, more frequent severe pain, greater workplace activity limitations, fewer hours of work, less coworker support, and greater arthritis-work spillover reported greater job strain. CONCLUSION: The findings underscore the diverse areas that contribute to perceptions of job strain and suggest that existing models of job strain do not adequately capture the stress experienced by individuals working with chronic illnesses or the factors associated with job strain. Measures similar to the CIJSS can enhance the tools researchers and clinicians have available to examine the impact of arthritis in individuals' lives.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".