Addressing Non-Communicable Diseases in the Western Cape, South Africa
Bibliographic record
Abstract
Background Chronic non-communicable diseases (CNCDs) are increasing with grave consequences to countries’ development. The purpose of this study was three-fold: (1) to determine challenges PURE study participants faced regarding CNCD interventions and what they required from a CNCD intervention programme, and (2) to explore courses of action Department of Health (DoH) officials thought would perform best, as well as (3) to determine what DoH officials perceive to be obstacles in addressing the CNCD epidemic. Design and methods A subsample of 300 participants from the Prospective Urban and Rural Epidemiological study's Western Cape urban cohort and six key officers from the DoH were recruited to participate in this cross-sectional study. Questionnaires were used in face-to-face interviews with the PURE study participants and DoH officials, together with the multi-criteria mapper (MCM) interviewing method with the latter. Results Most PURE participants were overweight/obese, but not keen to participate in weight loss interventions. They sought education on foods associated with weight gain, shopping lists, cooking lessons and recipes from CNCD intervention programmes. Department of Health officials regarded the integration of health services, community participation, amongst others as the most favourable options to address the CNCD epidemic. Conclusions The integration of health services, community participation, food taxation and improving inter-sectoral partnerships were viewed as the most feasible options to address the CNCD epidemic according to the DoH officials. At community level, the needs for education and practical hints were expressed. Current CNCD interventions should be adapted to include the context-based needs of communities.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".