A Cross-Sectional Survey of the Prevalence and Determinants of Overweight and Obesity Among Primary Healthcare Professional Nurses In Eastern Cape, South Africa
Bibliographic record
Abstract
There is a paucity of data on the burden of obesity among primary Health care professional nurses in Eastern Cape Province of South Africa. This study examines the prevalence and determinants of overweight and obesity among Primary Healthcare Professional nurses in Eastern Cape Province of South Africa. This workplace cross-sectional study was conducted among 203 Primary Health Care Professional nurses selected conveniently across 41 primary healthcare facilities in Eastern Cape, South Africa following a the WHO STEPwise approach and using the WHO STEPwise questionnaire for data collection. Data were expressed as mean, counts and proportions, as appropriate. We compared percentages using chi-square test. Descriptive and inferential statistics were conducted. Seventy six percent of the nurses were obese and 18% were overweight. Age, gender, marital status, duration of practice, alcohol use and smoking were significantly associated with obesity. Only age >30 years and not using alcohol were independent predictors of obesity, after adjusting for confounders. We found a high prevalence of obesity among primary healthcare professional nurses in this setting. This constitute future risk for an increased prevalence of chronic diseases among the healthcare workforce in this setting. There is a need for measures to promote healthy lifestyle behaviours and weight management among nurses in this 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 | 0.001 | 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 | 0.000 |
| 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".