From advocacy to action: civil society and development agencies engaging the private sector actors to improve nutrition in Africa
Bibliographic record
Abstract
Background Africa has a triple burden of malnutrition. The private sector can affect the nutritional status of the population. To improve nutrition, civil society and development agencies are developing initiatives to engage these actors. The objectives of this study were to (a) identify and describe these initiatives and (b) understand their successes and challenges. Methods An exploratory research design, including an online search, the author’s knowledge, and generative artificial intelligence, was used to develop a list of potential nutrition initiatives. Publicly available data on these initiatives was included in an Excel template. Initiatives with a nutrition focus were shortlisted using an inclusion and exclusion criterion. In-depth review of data and semi-structured interviews were conducted with shortlisted nutrition initiatives for further insights. Results Forty-eight initiatives were identified. Of these, twenty-four were multi-country with African presence, and twenty-four were Africa-only. Eight initiatives were shortlisted for in-depth review. Three more were added based on advice from an interviewee. Most initiatives were founded between 2011 and 2015. Private sector actors of varied sizes, operating in diverse food value chains, were engaged by the lead agencies. However, these actors were focused on food processing and manufacturing, with only some initiatives engaging the food retailers. The civil society and development agencies worked with the private sector through convening meetings, collaboration on projects, capacity building through training, and encouraging the private sector to make public commitments and monitoring them. Frequently reported initiative successes included an increased recognition by governments on the need to engage with the private sector on nutrition improvements. Frequently shared challenges were limited resources (financial and human) and an unclear business rationale to invest in nutrition. Key recommendations for the future were to ensure an appropriate structure with the right partners, an aligned vision, a robust governance process, and regular communication. Conclusion Multi-country initiatives led by civil society organisations or development agencies are engaging the private sector to improve nutrition in Africa. These initiatives operate using different approaches to influence private sector actions. This study fills an important knowledge gap by identifying and describing such initiatives and presenting their successes and challenges for future initiatives design and execution.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".