OMERACT Filter Evidence Supporting the Measurement of At-work Productivity Loss as an Outcome Measure in Rheumatology Research
Bibliographic record
Abstract
OBJECTIVE: Indicators of work role functioning (being at work, and being productive while at work) are important outcomes for persons with arthritis. As the worker productivity working group at OMERACT (Outcome Measures in Rheumatology), we sought to provide an evidence base for consensus on standardized instruments to measure worker productivity [both absenteeism and at-work productivity (presenteeism) as well as critical contextual factors]. METHODS: Literature reviews and primary studies were done and reported to the OMERACT 12 (2014) meeting to build the OMERACT Filter 2.0 evidence for worker productivity outcome measurement instruments. Contextual factor domains that could have an effect on scores on worker productivity instruments were identified by nominal group techniques, and strength of influence was further assessed by literature review. RESULTS: At OMERACT 9 (2008), we identified 6 candidate measures of absenteeism, which received 94% endorsement at the plenary vote. At OMERACT 11 (2012) we received over the required minimum vote of 70% for endorsement of 2 at-work productivity loss measures. During OMERACT 12 (2014), out of 4 measures of at-work productivity loss, 3 (1 global; 2 multiitem) received support as having passed the OMERACT Filter with over 70% of the plenary vote. In addition, 3 contextual factor domains received a 95% vote to explore their validity as core contextual factors: nature of work, work accommodation, and workplace support. CONCLUSION: Our current recommendations for at-work productivity loss measures are: WALS (Workplace Activity Limitations Scale), WLQ PDmod (Work Limitations Questionnaire with modified physical demands scale), WAI (Work Ability Index), WPS (Arthritis-specific Work Productivity Survey), and WPAI (Work Productivity and Activity Impairment Questionnaire). Our future research focus will shift to confirming core contextual factors to consider in the measurement of worker productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.123 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".