Work-related factors of presenteeism: The mediating role of mental and physical health.
Bibliographic record
Abstract
Even though work-related factors have been found to play a crucial role in predicting presenteeism, studies investigating established theoretical frameworks of job design features and, in particular, underlying mechanisms are still very scarce. The objective of this study was to investigate the influence of the areas of work life according to the Areas of Worklife Scale (AWS; Leiter & Maslach, 2004) on presenteeism. We examined mental and physical health as the underlying process of this relationship and assessed 2 presenteeism outcome measures and their relationship to each other-that is, the frequency of acts of presenteeism and work productivity. Using a cross-sectional design, the study was conducted in a sample of 885 employees from German public service. Results showed that the influence of some, but not all, areas of work life (workload, control, reward, and values) on both acts of presenteeism and health-related lost productivity was mediated by health indicators (well-being and musculoskeletal complaints). Moreover, we found a relationship between health-related lost productivity and acts of presenteeism. The present research clarifies the importance of work-related factors as antecedents of sickness presenteeism. The findings of our study also emphasize the necessity to include both acts of presenteeism and health-related lost productivity in presenteeism research and prevention. Presenteeism should be included as a measure in health prevention interventions because it reflects a crucial part of employee health that is not covered by other measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".