Elevated Psychosocial Stress at Work in Patients with Systemic Lupus Erythematosus and Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Psychosocial stress at work not only affects the healthy working population, but also workers with chronic diseases. We aimed to investigate the psychosocial work stress levels in patients with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). METHODS: A cross-sectional study applied the Effort-Reward Imbalance (ERI) questionnaire - an internationally established instrument that measures work stress - to patients with SLE and RA who were capable of work and to a group of controls without these diseases. Participants were recruited through rheumatologists in private practices, hospitals, and from self-help groups by personal communication, paper-based flyers, and online advertisements. Because very few studies tested the ERI's applicability in patient groups, with a lack of evidence in patients with inflammatory rheumatic diseases, internal consistency and construct validity of the ERI measure were evaluated. RESULTS: Data came from 270 patients with RA and 247 with SLE, and 178 controls. Patients showed elevated psychosocial stress at work compared to controls. Across the total sample and all groups, satisfactory internal consistencies of the scales effort, reward, and overcommitment were obtained (Cronbach's alpha coefficients > 0.70), and confirmatory factor analysis replicated the theoretical structure of the ERI model (goodness-of-fit index > 0.80). CONCLUSION: We found elevated psychosocial stress at work in patients with SLE and RA compared to controls by applying the ERI model. Despite some heterogeneity in the sample, we achieved satisfactory psychometric properties of the ERI questionnaire. Our results suggest that the ERI questionnaire is a psychometrically useful tool to be implemented in epidemiological studies of employed patients with SLE and RA.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".