Establishing work productivity loss norms: Absenteeism and presenteeism in a Canadian working population
Bibliographic record
Abstract
Cost-effectiveness guidelines recommend including productivity losses in economic evaluations conducted from a societal perspective. However, work productivity loss estimates, including absenteeism and presenteeism, are lacking for a general working population. This limits the ability of researchers and decision makers to comprehensively evaluate the incremental costs of health conditions and the benefits of interventions. Using the 2022 Canadian Community Health Survey, absenteeism was measured using two recall periods (7 days and 3 months). Presenteeism behaviour was measured as days worked while sick, and the related productivity loss was measured using a 0-10 scale and an hours method. Absenteeism and presenteeism estimates in a representative Canadian working population (n=9,148) were reported by age, sex, health status, and chronic conditions. The 0-10 scale (4.89% (standard error: 0.27%)) generated a higher presenteeism productivity loss percentage than the hours method (1.73% (0.18%)). Females reported higher absenteeism in the past 3 months, higher presenteeism behaviour, and higher presenteeism loss percentage (the 0-10 scale) than males; ages 30-44 reported the highest presenteeism loss percentage using the scale method (5.11% (0.40%)), whereas ages 15-29 reported the highest loss percentage using the hours method (2.02% (0.61%)). Health status was inversely related to absenteeism in the past 3 months and to presenteeism behaviour and related productivity loss percentages across all methods. These results underscore that productivity loss estimates differ by recall period and measurement method. This study generated population norms for absenteeism and presenteeism that can serve as benchmarks for these outcomes among specific groups relative to the general population. • Productivity losses are key in economic evaluations from a societal perspective • Estimates of absenteeism and presenteeism varied by recall and measurement method • Females reported higher absenteeism and presenteeism behaviour in the past 3 months • Health status was inversely related to productivity losses • Established productivity loss norms can serve as benchmarks for comparison
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".