One-year Predictors of Presenteeism in Workers with Rheumatoid Arthritis: Disease-related Factors and Characteristics of General Health and Work
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) affects adults of working age and leads to productivity losses because of presenteeism that results from limitations while at work. The aim of our study was to gain insight into disease-related factors, general health, and work characteristics as predictors of presenteeism in workers with RA. METHODS: Workers with RA (n = 150) recruited by rheumatologists completed questionnaires at baseline and after 1 year. Medical information was retrieved from patient records. Presenteeism was measured by the Work Limitations Questionnaire. Disease [28-joint Disease Activity Score (DAS28), Health Assessment Questionnaire (HAQ), pain, fatigue], general health (mental, physical, deterioration of health), and work characteristics (work instability, social support, workload) were assessed as predictors of presenteeism after 1 year using linear regression analyses. RESULTS: Presenteeism was 4.0 h over a 2-week period based on an average work week of 28.7 hours. More RA-related disability (HAQ; B = -1.20, 95% CI -2.12 to -0.28), poorer mental health (B = -0.04, 95% CI -0.08 to -0.01), and health deterioration over a 1-year period (B: -0.02, 95% CI -0.04 to -0.01) were associated with more presenteeism. Work characteristics were not associated with presenteeism. CONCLUSION: Disease-related factors and general health characteristics were significantly associated with presenteeism at 1-year followup, although the effects of the general health characteristics were considered not to be relevant. To reduce presenteeism and improve functioning at work, it is important to pay attention to reducing RA-related disability in addition to reducing disease activity. A broader perspective is needed and should also take into account the level of RA-related disability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".