An assessment of the utilisation of stokvels or rotating savings and credit associations to influence healthy eating in South Africa
Bibliographic record
Abstract
Background In South Africa, the prevalence of non-communicable diseases (NCDs) is rising alarmingly, closely linked to dietary habits shaped by socioeconomic conditions. Stokvels, traditional community-based savings groups, emerge as potential catalysts for nutritional intervention, particularly among urban populations facing the dual challenges of undernutrition and obesity. This extensive study delves into the roles of stokvels in improving food security, promoting health, and empowering women within the urban landscapes of sub-Saharan Africa. Methods The research utilized a mixed-method approach to investigate the role of stokvels in urban sub-Saharan Africa comprehensively. Firstly, a systematic literature review was conducted, meticulously analyzing 28 studies that delved into the socio-economic impacts of ROSCAs and ASCAs, providing a foundational understanding of the role of stokvels in the region. This was complemented by stakeholder mapping, where indepth interviews with 21 key stakeholders were conducted to identify the macro-level influencers on stokvel operations. These influencers included government policies, economic conditions, cultural norms, and technological advancements, offering a nuanced view of the external factors impacting stokvel functionality. Further, the research employed a realist evaluation, involving 60 participants, including 20 stokvel leaders and 40 general members. This qualitative approach facilitated an understanding of the contextual factors influencing decision-making within stokvels, particularly concerning food purchasing and consumption patterns. Lastly, the study incorporated a discrete choice experiment (DCE), engaging 200 stokvel members. This quantitative method assessed the factors impacting members' preferences for healthy foods, analyzing their choices against various attributes related to food procurement and consumption. Results The study's results presented a multi-dimensional understanding of stokvels' influence. The systematic literature review highlighted stokvels as catalysts for disciplined savings and financial literacy, indirectly contributing to improved nutritional choices and food security. The stakeholder mapping revealed that stokvel operations are significantly influenced by external factors such as government policies, economic conditions, cultural norms, and technological advancements, which shape their efficacy in achieving financial and health-related goals. The realist evaluation provided deep insights into how grocery stokvels shape food purchasing decisions. It was found that these stokvels often employ bulk buying strategies, which ensure food security and affordability for their members. This approach reflects the stokvels' emphasis on strategic, collective decisionmaking that prioritizes the welfare of all members. Lastly, the discrete choice experiment highlighted members' preferences regarding healthy food options. The study revealed a preference for cost-effective shopping options like "two-for-one" offers. It also underscored the significant role of household decision-makers in influencing food choices, showing a complex interplay between socio-economic status, cultural norms, and individual preferences in dietary habits. This aspect of the study offered valuable insights into the behavioural economics of food choice within the context of stokvels. Conclusion The comprehensive analysis concludes that stokvels are vital components of the urban food systems in sub-Saharan Africa. They significantly impact women's health and economic empowerment by influencing healthier eating habits and facilitating community engagement in health promotion. However, the effectiveness of stokvels is subject to various challenges, including economic constraints and the need for supportive infrastructural and policy frameworks. Recommendations To enhance the positive impact of stokvels, it is recommended that government policies should provide more robust support and integration of these groups into the formal economic framework. Health promotion strategies need to incorporate stokvels as platforms for disseminating nutritional education and influencing food choices. Future research should extend to other African nations, exploring the varied impacts of stokvels in different cultural and economic contexts to develop comprehensive, culturally sensitive, and region-specific health and nutritional interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".