A 6-year prospective study on work participation and the associated factors in Dutch patients with systemic lupus erythematosus (SLE)
Bibliographic record
Abstract
BACKGROUND: Previous studies showed that many patients with SLE do not have paid work. However, having paid work is important for self-esteem, social contacts and income. It is therefore important to understand the characteristics contributing to work status and in particular identifying modifiable variables to help patients with SLE and their employers to maintain work. The objective of this study is to investigate associations of demographic, disease-related and work characteristics with having and maintaining paid work for ≥ 5 years in patients with SLE over a six-year period. METHODS: All patients diagnosed with SLE, independent of disease duration and under treatment in Amsterdam UMC location VUmc or Reade were invited to participate in the longitudinal Amsterdam SLE cohort (2007-2018). Demographic, disease-related and work characteristics of these patients were analysed. Baseline was defined as the time of study entrance. Generalized Estimating Equations with logit link function were used to identify associations between these characteristics on having paid work. Logistic regression was used to study associations with maintenance of work and work disability. RESULTS: At baseline, 52% of patients (114/220) were employed, which decreased to 46% (73/157) after six years. The main reported reason for unemployment was because of SLE-related symptoms (63%). Among the 106 patients who were unemployed at baseline, 16% (17/106) gained work during follow-up of whom 47% (8/17) also maintained work. Of the 114 patients employed at baseline, 29% (33/114) remained employed throughout the entire six-year follow-up period. Having paid work over time was associated with younger age, higher level of education, shorter disease duration, lower organ damage and supervisor support. Maintaining employment for ≥ 5 years during follow-up was associated with regular working hours, skill discretion, decision authority and decision latitude. A longer disease duration was associated with work disability at baseline. CONCLUSIONS: This study shows that alongside demographic and disease-related characteristics, also work characteristics are associated with having and maintaining paid work in SLE patients. These characteristics should be taken into consideration when developing interventions to improve sustainable employability in patients with SLE.
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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 | 0.002 |
| 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 | 0.001 |
| 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".