Health care providers’ perspectives of diet-related non-communicable disease in South Africa
Bibliographic record
Abstract
BACKGROUND: In South Africa, diet-related non-communicable diseases (dr-NCDs) place a significant burden on individuals, households and the health system. In this article, we investigate the experiences of eight key informants within the public sector health care system (nurse, doctor and dietician), in order to reflect on their experiences treating dr-NCDs. METHODS: We interviewed eight key informants who were central to the primary care service for at least 40,000 people living in a low-income neighbourhood of Cape Town, South Africa. In previous work, we had interviewed and conducted ethnographic research focused on dr-NCDs in the same neighbourhood. We then conducted a thematic analysis of these interviews. RESULTS: The perspectives of key informants within the public sector therefore offered insights into tensions and commonalities between individual, neighbourhood and health systems perspectives. In particular, the rising prevalence of dr-NCDs alarmed providers. They identified changing diet as an important factor driving diabetes and high blood pressure in particular. Health care practitioners focused primarily on patients' individual responsibility to eat a healthy diet and adhere to treatment. A marked lack of connection between health and social services at the local level, and a shortage of dieticians, meant that doctors provided rapid, often anecdotal dietary advice. The single dietician for the district was ill-equipped to connect dr-NCDs with the upstream determinants of health. While providers often had empathy and understanding of patients' circumstances, their training and context had not equipped them to translate that understanding into a clinical context. Providers seemingly could not reconcile their empathy with their perception of dr-NCDs as a failure of prudence or responsibility by patients. Significant shortcomings within health systems and social services make reflexive practice very difficult. CONCLUSIONS: Supporting health care providers in understanding context, through approaches such as translational competency, while strengthening both health and social services, are vital given the high burden of NCDs in South Africa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".