Screening for Cardio-Metabolic Risk Factors Among Student Nurses: A Cross-Sectional Study
Bibliographic record
Abstract
Non-communicable diseases are a growing public health phenomenon in both developed and developing countries. This study examines the prevalence and correlates of cardio-metabolic risk factors among student nurses at a nursing college in East London, South Africa. The WHO STEPwise standardized questionnaire was used to collect information on socio-demographic data and behavioural characteristics (smoking, alcohol consumption, physical inactivity, and dietary intake) of 228 nursing students. Height, weight, waist circumference, blood pressure and fasting blood glucose were measured. The prevalence of overweight, obesity pre-diabetes and diabetes was 33%, 44%, 6% and 7%, respectively. Pre-hypertension and hypertension occurred in 44% and 11%, respectively. Female gender and increasing age were independent predictors of obesity. In the logistic regression model analysis, participants who were above 35 years [AOR=9.12, CI 3.37-24.68, p<0.000], female [AOR=4.10, CI 1.94-8.64, p=0.000], and do not meet the WHO sport criteria of physical activity participation [AOR=2.11, CI=1.10-4.07, p=0.025] had the likely odds of obesity. Interventions targeting physical activity and healthy lifestyle behavioural programmes to promote the health and wellness of the nursing students would improve the metabolic health of the nurses in the setting.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".