The evolution of non-communicable diseases policies in post-apartheid South Africa
Bibliographic record
Abstract
BACKGROUND: Redressing structural inequality within the South African society in the post-apartheid era became the central focus of the democratic government. Policies on social and economic transformation were guided by the government's blueprint, the Reconstruction and Development Programme. The purpose of this paper is to trace the evolution of non-communicable disease (NCD) policies in South Africa and the extent to which the multi-sectoral approach was utilised, while explicating the underlying rationale for "best buy" interventions adopted to reduce and control NCDs in South Africa. The paper critically engages with the political and ideological factors that influenced design of particular NCD policies. METHODS: Through a case study design, policies targeting specific NCD risk factors (tobacco smoking, unhealthy diets, harmful use of alcohol and physical inactivity) were assessed. This involved reviewing documents and interviewing 44 key informants (2014-2016) from the health and non-health sectors. Thematic analysis was used to draw out the key themes that emerged from the key informant interviews and the documents reviewed. RESULTS: South Africa had comprehensive policies covering all the major NCD risk factors starting from the early 1990's, long before the global drive to tackle NCDs. The plethora of NCD policies is attributable to the political climate in post-apartheid South Africa that set a different trajectory for the state that was mandated to tackle entrenched inequalities. However, there has been an increase in prevalence of NCD risk factors within the general population. About 60% of women and 30% of men are overweight or obese. While a multi-sectoral approach is part of public policy discourse, its application in the implementation of NCD policies and programmes is a challenge. CONCLUSIONS: NCD prevalence remains high in South Africa. There is need to adopt the multi-sectoral approach in the implementation of NCD policies and programmes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".