Between support and scepticism: Health professionals’ perceptions of a nutrition education program promoting low-carbohydrate, high-fat diets in under-resourced South African communities
Bibliographic record
Abstract
The burden of non-communicable diseases (NCDs) continues to rise, emphasizing the need for effective dietary interventions. Programs such as Eat Better South Africa (EBSA) advocate for low-carbohydrate high-fat (LCHF) dietary choices, especially in disadvantaged communities. However, the adoption of such approaches among healthcare professionals remains contentious. This qualitative study explores healthcare professionals' perspectives on nutrition and the EBSA program, drawing on 16 in-depth individual interviews with physicians, nurses, and dietitians from False Bay Hospital, Groote Schuur Hospital, and a primary health clinic in Hout Bay, Western Cape, South Africa. Thematic analysis of the interviews revealed four main findings. Healthcare professionals lacked confidence in their nutritional knowledge, and while many were familiar with the LCHF diet, opinions varied regarding its sustainability and health implications. Concerns were raised about the high fat content and affordability of LCHF foods. Professionals acknowledged the value of group support in behaviour change, as promoted by EBSA, but expressed reservations about its strong emphasis on LCHF diets. Key challenges identified for patients included poverty, cultural beliefs, limited education, and access to nutritious foods. The findings highlight a reliance on traditional dietary advice, with uncertainties about the feasibility and affordability of LCHF diets. These findings offer novel insights into the complexities of implementing community-based dietary interventions in South Africa, with implications for policy and practice.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".