Bibliographic record
Abstract
Traditionally, productivity loss has been measured according to illness-related absence from work (absenteeism) only. However, there is increasing evidence that presenteeism (reduced intensity and/or quality of labour input) is an even greater source of productivity losses. In addition to empirical measurement issues, there are theoretical issues with regard to productivity valuation. The traditional human capital method assumes that the value of productivity loss to society should be measured as the present values of lost time according to the wage, which is supposed to be equal to the marginal productivity of labour in a competitive labour market. The alternative, friction cost method, is based on the same assumption except that it adjusts for unemployment. However, these methods ignore the effects of risk aversion and team production which cause the wage to be lower than the marginal productivity. Existing productivity questionnaires did not capture sufficient information to enable the proper measurement and valuation of productivity loss from a societal perspective. A new questionnaire, Valuation of Lost Productivity (VOLP), was developed to capture all the time input loss components(absenteeism, presenteeism, employment status changes, and unpaid work productivity loss) as well as information on job and workplace characteristics, based on which wage multipliers can be calculated to value the productivity loss attributable to the reduced time input of workers. The thesis provides evidence for the validity of the VOLP in measuring time input loss due to poor health and its feasibility in evaluating the treatment effect on productivity in people with rheumatoid arthritis. The equality between wage and marginal productivity was tested using the Workplace and Employ Survey, a linked employer-employee database in Canada. Some evidence suggests that team workers are underpaid compared with their relatively higher iii productivity. In small firms, higher absenteeism results in lower productivity and wage, and the marginal productivity loss with respect to team worker absenteeism is higher than the wage loss. Furthermore, for team workers, health-related frequent reduction at work results in lower productivity and the resulted productivity loss is more than the wage differentials.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".