Presenteeism and job satisfaction among hospital environmental service workers during the COVID-19 pandemic: A moderated mediation model
Bibliographic record
Abstract
Presenteeism, i.e. attending work while ill, is known to be more prevalent among employees with a higher workload and/or job strain. Recently, the COVID-19 pandemic had a significant impact on the workload and strain experienced by environmental service workers (ESW). Drawing on both the Effort-Recovery Model and Organizational Support Theory, the model presented in this study evaluates the direct and indirect relationships (i.e. through depressive symptoms) between presenteeism and job satisfaction amongst ESW amid the pandemic. It also evaluates the moderating role of perceived organizational support in this first relationship. A small sample of ESW working in a Canadian hospital participated in this exploratory study by completing an electronic questionnaire. The results show three main findings. First, presenteeism was positively related to depressive symptoms and negatively related to job satisfaction. Second, depressive symptoms were found to be one of the mechanisms through which presenteeism exerted its effect on job satisfaction. Third, perceived organizational support was found to moderate the negative relationship between depressive symptoms and job satisfaction, so this relationship became weaker as perceived organizational support increased. Overall, this pilot study is one of few that have focused on the experience of hospital-based ESW, and the pandemic's effects on their well-being.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".