Presenteeism in small and medium-sized enterprises: Determinants and impacts on health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Small and medium sized enterprises are yet uncharted territory in terms of presenteeism. In addition, the Demand-Control-Support (DCS) and Siegrist's Effort-Reward Imbalance (ERI) models are proposed to predict stress-related health problems, but not for sickness behaviors such as presenteeism. OBJECTIVE: This study aims to examine the relationships between psychosocial risk factors, presenteeism, mental and physical health in the context of small and medium-sized enterprises (SMEs). This study also examines the moderating effect of company size on these associations. METHODS: To test the association between psychosocial risks, presenteeism, and health, only people working in small and medium-sized enterprises (SMEs) of between 2 and 199 employees were included in the sample, giving a total of 2,525 SME employees surveyed. To test the differences in exposure to psychosocial risk and presenteeism, and the moderating impact of size on the relationship between psychosocial risks, presenteeism, and health, we took the original sample (4608) of the EQCOTESST. RESULTS: The results confirm the associations between job demands, social support and effort-reward imbalance, and presenteeism. Also, the associations between presenteeism and health problems in SMEs' context. Multi-group analyses show that the business's size does not moderate the strength of the relationships between psychosocial risks, presenteeism and health. CONCLUSION: The current study highlights that SMEs are somehow protected from certain psychosocial constraints such as high job demands, and low social support, but are more exposed to others such as effort-reward imbalance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it