Measuring Worker Productivity: Frameworks and Measures
Bibliographic record
Abstract
Worker productivity is a combination of time off work (absenteeism) due to an illness and time at work but with reduced levels of productivity while at work (also known as presenteeism). Both can be gathered with a focus on application as a cost indicator and/or as an outcome state for intervention studies. We review the OMERACT worker productivity groups' progress in evaluating measures of worker productivity for use in arthritis using the OMERACT filter. Attendees at OMERACT 9 strongly endorsed the importance of work as an outcome in arthritis. Consensus was reached (94% endorsement) for fielding a broader array of indicators of absenteeism. Twenty-one measures of at-work productivity loss, ranging from single item indicators to multidimensional scales, were reviewed for measurement properties. No set of at-work productivity measures was endorsed because of variability in the concepts captured, and the need for a better framework for the measurement of worker productivity that also incorporates contextual issues such as job demands and other paid and unpaid life responsibilities. Progress has been made in this area, revealing an ambivalent set of results that directed us back to the need to further define and then contextualize the measurement of worker productivity.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".