Influences of 12-Hour Shifts on Unhealthy Eating Habits of Acute Care Nurses
Bibliographic record
Abstract
Shift work is a necessary part of a nurses’ work schedule to patients who need 24 hours of continuous care. Eating habits of nurses are influenced by working long hours, not having adequate time to take a meal break, and not having access to healthy food choices which can cause stress and exhaustion and results in weight gain and obesity. The purpose of this quantitative logistic regression analysis, guided by the health belief model and the theory of planned behavior, was to determine if there was a relationship between shift work and the unhealthy eating habits/obesity rates of acute care nurses. Data were gathered from the Nurses’ Study 3, which includes information from nurses or nursing students from the United States and Canada. The sample was 8988 nurses who worked 12-hour shifts and more than 20 hours per week. The results demonstrated that the relationship between working 12-hour shifts and unhealthy eating was not statistically significant (p = 0.39) the relationship between working 12-hour shifts and obesity rates were not statistically significant (p = 0.32). Further studies are needed to determine how often nurses eat or eat while working during their shift because of perceived inability to take scheduled breaks because of workloads. The study findings provide evidence for health professionals to examine their eating habits and modify healthy eating behaviors to maintain a healthy lifestyle. When nurses learn to care for themselves, nurses are positive role models for their patients by encouraging healthy lifestyles which effects positive social change.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".