Nurses working the night shift: Impact on home, family and social life
Bibliographic record
Abstract
Objective: To gain an understanding of the experience of registered nurses working the night shift, the impact on life outside of work, and ways of coping with home, family, and social stressors. A review of literature indicted that physiological and social difficulties from night shift work include problems with sleep, diet, menstrual cycles, stress/anxiety, weight gain, workplace errors and driving accidents. Also reported was less time for leisure, domestic responsibilities, child care, friends and family. Studies have been conducted internationally wherein workplace and cultural differences may affect global applicability. Interventions and anticipatory guidance are lacking. Further research was needed to better understand the effects on personal life and ways of coping. Methods: A qualitative, phenomenological method was utilized. Registered nurses (N = 21) were interviewed. Results: Identified themes included issues that affected family life, child care, and relationships with spouse/significant other, friends and extended family. Recommendations for self-care, coping, and suggestions for novice night shift nurses were offered. Conclusions: Twenty-one informants described the consequences of working the night shift and listed strategies used to contend with the stress it generates in their homes, families, and social lives. Nurses entering night shift employment would benefit from a program of anticipatory guidance. Knowledge concerning this topic raises awareness for improvements in nursing school curricula, institutional policy and staff satisfaction. Nursing remediation may involve scheduling flexibility, planned rest periods in comfortable staff lounges, healthy workplace nutritional offerings, exercise options, childcare services, peer support groups and in-service programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".