Productivity Loss Due to Presenteeism Among Patients with Arthritis: Estimates from 4 Instruments
Bibliographic record
Abstract
OBJECTIVE: To estimate and compare lost work hours attributable to presenteeism, defined as reduced productivity while working, in individuals with osteoarthritis (OA) or rheumatoid arthritis (RA), according to 4 instruments. METHODS: In our prospective study, 250 workers with OA (n = 130) or RA (n = 120) were recruited from community and clinical sites. Lost hours due to presenteeism at baseline were estimated using the Health and Labor Questionnaire (HLQ), the Work Limitations Questionnaire (WLQ), the World Health Organization's Health and Work Performance Questionnaire (HPQ), and the Work Productivity and Activity Impairment Questionnaire (WPAI). Only those respondents working over the past 2 weeks were included. Repeated-measures ANOVA was used to compare the lost-time estimates, according to each instrument. RESULTS: Of the 212 respondents included in the analyses, the frequency of missing and "0" values among the instruments was different (17% and 61% for HLQ, 8% and 5% for WLQ, 1% and 16% for HPQ, 0% and 27% for WPAI, respectively). The average numbers of lost hours (SD) per 2 weeks due to presenteeism using HLQ, WLQ, HPQ, and WPAI were 1.6 (3.9), 4.0 (3.9), 13.5 (12.5), and 14.2 (16.7). The corresponding costs for the 2-week period were CAN$30.03, $83.05, $284.07, and $285.10. The differences in the lost-hour estimates according to instruments were significant (p < 0.001). CONCLUSION: Among individuals with arthritis, estimates of productivity losses while working vary widely according to the instruments chosen. Further research on instrument design and implications for a standardized approach to estimate lost time due to presenteeism is needed.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".