From advocacy to action: civil society and development agencies engaging the private sector actors to improve nutrition in Africa
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».