Presenteeism and absenteeism: Differentiated understanding of related phenomena.
Bibliographic record
Abstract
In the past it was assumed that work attendance equated to performance. It now appears that health-related loss of productivity can be traced equally to workers showing up at work as well as to workers choosing not to. Presenteeism in the workplace, showing up for work while sick, seems now more prevalent than absenteeism. These findings are forcing organizations to reconsider their approaches regarding regular work attendance. Given this, and echoing recommendations in the literature, this study seeks to identify the main behavioral correlates of presenteeism and absenteeism in the workplace. Comparative analysis of the data from a representative sample of executives from the Public Service of Canada enables us to draw a unique picture of presenteeism and absenteeism with regards not only to the impacts of health disorders but also to the demographic, organizational, and individual factors involved. Results provide a better understanding of the similarities and differences between these phenomena, and more specifically, of the differentiated influence of certain variables. These findings provide food for thought and may pave the way to the development of new organizational measures designed to manage absenteeism without creating presenteeism.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".