At work but ill: psychosocial work environment and well‐being determinants of presenteeism propensity
Bibliographic record
Abstract
Many studies have shown that an unfavourable psychosocial environment increases the risk of mental and physical illness, as well as absenteeism, or sickness absence. However, more costly than absenteeism is presenteeism, where a person is present at work even though disabled by a mental or physical illness. We sought to identify factors explaining why workers would come to work even when their health is impaired. In a cross‐sectional design data were collected from 3825 employees of a Canadian organisation. The results show a high occurrence of presenteeism: workers went to work in spite of illness 50% of the time. Presenteeism propensity (the percentage of days worked while ill over total number of sick days) was higher for workers who were ill more often. Heavier workloads, higher skill discretion, harmonious relationships with colleagues, role conflict and precarious job status increased presenteeism, but decision authority did not. Workers reporting high psychological distress and more severe psychosomatic complaints were also more likely to report higher rates of presenteeism. These results suggest that stress research should not only include absenteeism as an outcome indicator, but also consider 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".