The impact of 12-hour shifts on nurses’ health, wellbeing, and job satisfaction: A systematic review
Bibliographic record
Abstract
Objective: This review was conducted to investigate the impact of applying 12-hour shifts in comparison to 8-hour shifts on nurses’ health wellbeing and job satisfaction.Methods: MEDLINE, CINHALE, PsycINFO, EMBASE, Web of Science, and SCOPUS databases were searched, covering the period between 1980 to 2017. Studies were included if they concerned nurses working for 12-hour shifts in comparison to 8-hour shifts in hospital settings, based on observational/surveys studies.Results: In the yielded 12 studies, 3 studies reported that 12-hour shifts had an impact on nurses’ health and wellbeing, such as cognitive anxiety, musculo-skeletal disorders, sleep disturbance, and role stress; however, there was no significant difference between 12- and 8-hour shifts with digestive and cardiovascular disorders, psychological ill health, and somatic anxiety. Of the 4 studies measuring the impact of 12-hour shifts on fatigue, three studies showed that the nurses experienced more fatigue in the 12-hour shifts in comparison to 8-hour shifts; nevertheless, one study did not find a significant difference in fatigue and critical thinking performances between 12- and 8-hour shifts. Nine of the 12 studies measured job satisfaction in 12- and 8-hour shifts, 5 studies showed a greater dissatisfaction regarding 12-hour shifts, while 3 studies found that the nurses were more satisfied with 12-hour shifts than with 8-hour shifts; but one study pointed out that there was a difference between the two shifts considering pay and professional status.Conclusions: The findings of the review suggest that 12-hour shifts resulted in negative health concerns and job dissatisfaction; however, there is a need for more empirical evidence to support this.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".