Dietary Behaviour Is Associated with Cardiometabolic and Psychological Risk Indicators in Female Hospital Nurses—A Post-Hoc, Cross-Sectional Study
Bibliographic record
Abstract
Unfavourable dietary behaviours of female nurses, especially among shift-working nurses, including high snacking frequency, short fasting period and large day-to-day energy intake variability may be linked with adverse health. In this study we: (1) examined the relationship between dietary behaviour and cardiometabolic and psychological health in female nurses; and, (2) compared dietary behaviour, cardiometabolic and psychological health between shift-working and non-shift-working female nurses. A total of 73 nurses had their cardiometabolic health indicators evaluated and completed psychological health questionnaires; 55 completed a 3-day dietary log. Associations between dietary behaviour and health measures were examined using Spearman’s partial correlation analysis. Analysis of covariance (ANCOVA) was used to compare dietary behaviour and health indicators between shift- and non-shift-working nurses. The majority of snacks consumed by nurses (70%) were unhealthy snacks (e.g., chocolate and chips), and higher snacking frequency was associated with greater percent body fat (r(50) = 0.287, p = 0.039), and worse mood-tension (r(48) = 0.327, p = 0.021) and anger-hostility (r(48) = 0.289, p = 0.042) scores. Day-to-day energy intake variability was positively associated with body mass index (BMI, r(50) = 0.356, p = 0.010) and waist circumference (r(50) = 0.283, p = 0.042). Shift-working nurses exhibited shorter fasting duration, larger day-to-day energy intake variability and higher total mood disturbance score when compared to their non-shift-working colleagues (all p < 0.05). The results of the present study suggested that addressing dietary behaviours may improve the cardiometabolic and psychological health of female nurses. Shift-working nurses may require a more specific dietary program to improve their psychological health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".