The Lifestyle Habits and Cardiovascular Disease Risk Factor Profiles of Staff Within a Provincial Tertiary Institution in South Africa
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is a rising burden in many parts of the world including South Africa. There is a strong relationship between CVD, type II diabetes and obesity. The CVD risk and health status of employees in tertiary institutions remains of high concern, as limited studies have been conducted in this area.OBJECTIVES: The study objectives were to determine the: CVD risk factor profile; possible risks of other NCDs; and lifestyle habits i.e. physical activity, nutritional/dietary habits, tobacco smoking and alcohol consumption of university employees.METHODS: A cross-sectional design was conducted on academic and non-academic (professional) staff members from the University of KwaZulu-Natal, South Africa. Purposive sampling of staff within the School of Health Sciences were selected to participate in this study. The data collection included health/fitness screening questionnaires, selected anthropometric measurements and non-fasting blood tests.RESULTS: Seventy-five staff members with a mean age of 39.71 years old voluntarily participated in this study. A significant number of staff members presented with a moderate CVD risk profile. Body fat percentages were significantly higher for females than for males. In addition waist circumference measures indicated possible risks for Type II diabetes and metabolic syndrome. The overall lifestyle habits were adequate to poor.CONCLUSION: There is an imperative need for workplace health interventions to improve CVD risk profiles and the susceptibility to other NCD’s among staff in tertiary institutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".