Perceived Social Support and Presenteeism Among Nurses
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore the mediating role of psychological capital in the relationship between perceived social support and presenteeism among nurses. BACKGROUND: The concept of presenteeism explored in this study refers to the behavior of nurses who hold on to their jobs despite poor physical or mental health, manifested in poor work productivity and loss of productivity. Perceived social support and psychological capital may help reduce presenteeism. However, there is limited knowledge about the association between perceived social support, psychological capital, and presenteeism among nurses. METHODS: Data were collected through questionnaires from 468 RNs. Data analysis used Pearson's correlation analysis, multiple linear regression, and structural equation model. RESULTS: The results indicated that perceived social support and psychological capital were significantly negatively correlated with nurses' presenteeism. Structural equation modeling revealed that psychological capital mediated the relationship between perceived social support and presenteeism, with a partial mediating effect of -0.191, accounting for 28% of the total effect. CONCLUSIONS: These results identified structural relationships between the 3 variables of perceived social support, psychological capital, and presenteeism and provided a theoretical reference for developing strategies to decrease nurses' 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".