Understanding Presenteeism in Patients With Ankylosing Spondylitis: Contributing Factors and Association With Sick Leave
Bibliographic record
Abstract
OBJECTIVE: To understand the impact of ankylosing spondylitis (AS) on presenteeism and to explore its relationship to sick leave. METHODS: AS patients completed a questionnaire consisting of sociodemographics, disease characteristics, and work outcomes, including sick leave and presenteeism, assessed by the Work Limitations Questionnaire (WLQ). Associations between a broad range of explanatory variables with the WLQ and AS-related sick leave were assessed by zero-inflated negative binomial and zero-inflated Poisson regressions. RESULTS: Of 311 employed patients (204 men [65.6%]), 18% had sick leave in the past month. Limitations in meeting time management demands (33.7%), physical demands (30.2%), mental-interpersonal demands (20.2%), and output (19.0%) were noted. With the mean ± SD WLQ index score of 6.7 ± 5.9, the average decrease in work productivity attributable to health was 6.3%; an extra 7.1% of work hours would be needed to compensate for lost productivity. Helplessness, female sex, and impaired health-related quality of life (Ankylosing Spondylitis Quality of Life instrument [ASQoL]) were major contributors to the level of presenteeism (P < 0.01). At-work limitations (WLQ) and lower quality of life (ASQoL) were significantly associated with probability of sick leave, while the length of sick leave was strongly associated with lower educational level and helplessness (P < 0.01), and in some models, also with disease duration and country of residence (P < 0.05). CONCLUSION: AS hinders patients' work, mainly in time management and physical demand domains. The WLQ and ASQoL are able to identify patients who incur sick leave. Helplessness contributes independently to the level of presenteeism and the length of sick leave.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".