035. Comprehensibility of Global Measures for at-Work Productivity in Patients with Rheumatic Conditions: An International Qualitative Study
Bibliographic record
Abstract
Background: There are a number of global measures available to explore at-work productivity loss (presenteeism) in patients with rheumatic conditions. However, large variations in results are seen which might be partly attributed to differences in content across four aspects: constructs addressed, recall period used, disease attribution and reference. The purpose of this international qualitative study was to identify, from a patient’s perspective, difficulties and differences in the interpretation of five global measures of presenteeism across seven countries in patients with inflammatory arthritis and OA. Methods: 70 patients with a diagnosis of inflammatory arthritis or OA in paid employment were recruited from seven countries (UK, Sweden, France, Netherlands, Romania, Italy and Canada). Patients provided baseline demographic, clinical and occupational characteristics, after which a cognitive debriefing interview took place. Patients were randomly allocated to be interviewed on 3/5 global measures [Work Productivity Scale-RA (WPS-RA), Work Productivity and Activity Impairment Questionnaire (WPAI), Work Ability Index (WAI), Quality and Quantity questionnaire (QQ), and WHO Health and Performance Questionnaire (HPQ)], with the WPAI being asked for all patients as a standard measure of comparison between countries and patients. In addition, all five measures were given to the patients with the requirement of stating their most preferred measure. NVivo software was used to code the data into four themes: constructs, recall period, reference and attribution.
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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 | 0.032 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".