Measuring, Analyzing, and Presenting Work Productivity Loss in Randomized Controlled Trials: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: This study aimed to conduct a scoping review of randomized controlled trials (RCTs) and investigate which work productivity loss outcomes were measured in these RCTs, how each outcome was measured and analyzed, and how the results for each outcome were presented. METHODS: A systematic search was conducted from January 2010 to April 2020 from 2 databases: PubMed and Cochrane Central Register of Controlled Trials. Data on country, study population, disease focus, sample size, work productivity loss outcomes measured (absenteeism, presenteeism, employment status changes), and methods used to measure, report, and analyze each work productivity loss outcome were extracted and analyzed. RESULTS: We found 435 studies measuring absenteeism or presenteeism, of which 155 studies (35.6%) measured both absenteeism and presenteeism and were included in our final review. Only 9 studies also measured employment status changes. The most used questionnaire was the Work Productivity and Activity Impairment Questionnaire. The analysis of absenteeism and presenteeism data was mostly done using regression models (n = 98, n = 98, respectively) for which a normal distribution was assumed (n = 77, n = 89, respectively). Absenteeism results were most often presented in time whereas presenteeism was commonly presented using a percent scale or score. CONCLUSIONS: There is a lack of consensus on how to measure, analyze, and present work productivity loss outcomes in RCTs published in the past 10 years. The diversity of measurement, analysis, and presentation methods used in RCTs may make comparability challenging. There is a need for guidelines providing recommendations to standardize the comprehensiveness and the appropriateness of methods used to measure, analyze, and report work productivity loss in RCTs.
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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 | 0.165 | 0.488 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".