Shiftwork, mental health and performance among Indian nurses: the role of social support
Bibliographic record
Abstract
Purpose The purpose of this study is to explore the impact of mental health on the job performance among nurses, how shiftwork affects the impact and how social support alters it. Design/methodology/approach Data were collected through a questionnaire survey from 683 Indian nurses working in multiple hospitals in two major cities in Northern India. Descriptive statistics, correlations and hierarchical regressions were employed to investigate the links between job stress, emotional exhaustion and job performance along with the simultaneous moderating effects of shiftwork and social support on this relationship. Findings Both job stress and emotional exhaustion were negatively related to job performance. However, three-way interaction analysis revealed that social support moderated the above relationships differently between shift workers and day workers. Social support significantly altered the pattern of the relationship between the independent and dependent variables among day workers but had no impact in mitigating the relationship among shift workers. Research limitations/implications The findings endorsed the usefulness of the stress theory, burnout theory, the conservation of resources model and the social support resource theory in modeling the phenomenon and explaining the behavior of day workers but not that of shift workers. Practical implications It paved the way for evidence-based practices in health-care management. Originality/value This study extends theoretical predictions to India and demonstrates their global portability. It focuses on shiftwork and social support as simultaneous moderators, and through a unique three-way analysis, documents complex interaction patterns that have hitherto been unrecorded. It also brings scholarly attention to the nursing population in India whose organizational behavior is poorly documented in the empirical literature.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".