Sickness Presenteeism, Sickness Absenteeism, and Health Following Restructuring in a Public Service Organization
Bibliographic record
Abstract
abstract This study examined the relationship between sickness presenteeism, sickness absenteeism, organizational outcomes and employee health. In particular, we wanted to investigate to what degree employees were substituting sickness presence for sickness absence. Three hypotheses were tested to formalize this ‘substitution proposition’. We surveyed a Canadian public service organization which was involved in a large scale downsizing initiative. For this study, 237 Personnel Corporation (pseudonym used) employees responded to the survey, representing a 66 per cent response rate. Survey results indicated that, while the workforce was of average health, sickness absenteeism was less than half that of the national average. The difference could be accounted for by sickness presenteeism – the average number of days employees attended work while ill or injured was greater than the number of days of sickness absence. The pattern of results supported the notion that employees were substituting presenteeism for absenteeism. The frequency and type of self‐reported health problems were highly similar for presenteeism and absenteeism. Work factors (e.g. job security, supervisor support and job satisfaction) tested were significantly correlated with presenteeism. Presenteeism appears to be a stronger predictor of health than absenteeism, suggesting that efforts to improve workplace health may have a more immediate impact on presenteeism than on absenteeism.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".