Test-retest Reliability and Correlations of 5 Global Measures Addressing At-work Productivity Loss in Patients with Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: Several global measures to assess at-work productivity loss or presenteeism in patients with rheumatic diseases have been proposed, but the comparative validity is hampered by the lack of data on test-retest reliability and comparative concurrent and construct validity. Our objective was to test-retest 5 global measures of presenteeism and to compare the association between these scales and health-related well-being. METHODS: Sixty-five participants with inflammatory arthritis or osteoarthritis in paid employment were recruited from 7 countries (UK, Canada, Netherlands, France, Sweden, Romania, and Italy). At baseline and 2 weeks later, 5 global measures of presenteeism were evaluated: the Work Productivity Scale-Rheumatoid Arthritis (WPS-RA), Work Productivity and Activity Impairment Questionnaire (WPAI), Work Ability Index (WAI), Quality and Quantity questionnaire (QQ), and the WHO Health and Performance Questionnaire (HPQ). Agreement between the 2 timepoints was assessed using single-measure intraclass correlations (ICC) and correlated between each other and with visual analog scale general well-being scores at followup by Spearman correlation. RESULTS: ICC between measures ranged from fair (HPQ 0.59) to excellent (WPS-RA 0.78). Spearman correlations between measures were moderate (Qquality vs WAI, r = 0.51) to strong (WPS-RA vs WPAI, r = 0.88). Correlations between measures and general well-being were low to moderate, ranging from -0.44 ≤ r ≤ 0.66. CONCLUSION: Test-retest results of 4 out of 5 global measures were good, and the correlations between these were moderate. The latter probably reflect differences in the concepts, recall periods, and references used in the measures, which implies that some measures are probably not interchangeable.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".