A Novel Model of Pre‐competitive Public‐Private Collaboration for Nutrition Research for Vulnerable Populations
Notice bibliographique
Résumé
Background Investment in nutrition research suffers fragmentation between the public and private sectors, especially in connecting and mobilizing common cause interests for improving public health nutrition in vulnerable populations. Aim To propose a pre‐competitive, collaborative, multi‐stakeholder model for defining, funding and disseminating new research to advance nutrition science. The model is developed based on a compilation and critical review of existing models to support research across the spectrum of sectors in public health, such as drug and vaccine development, and exploring their application for advancement of nutrition research using pre‐competitive public‐private partnership platforms. Methods The process for development of the model platform began with the convening of a high‐level Research Advisory Group, comprised of an international panel of nutrition experts from nonprofits and academia to identify the key gap areas for research in public health nutrition. Concurrently, representatives from 10 private sector companies were engaged to help understand the challenges in the businesses for increasing investments in healthy products and services. The consultative process led to the identification of key research streams for immediate action as well as a series of mid‐and long‐term priorities. A review of literature on models used to advance pre‐competitive research allowed us to define a structure and governance model with the highest potential for success in engaging businesses towards public health outcomes, specifically to improve nutrition at scale in vulnerable populations in low and middle‐income countries. Results We determined several focus areas that specifically address key gaps in nutrition research which are pre‐competitive in nature including: Food Safety; Biomarkers, Bioavailability and health Diagnostics; and Behavior Change Communication. We also define rules of engagement (e.g. governance, funding structure, conflict resolution, expected roles and guiding principles) to ensure maximum reliability and objectivity linked to ensuring that nutritious and safe foods and products are accessible, purchased or received, and consumed by those with high risk of inadequate dietary intake, particularly in low and middle‐income countries. We also identify some of the challenges and successes in developing the platform as a proof of concept. Conclusion The kind of research delivered by this platform would meet two goals concurrently: 1) improving public policy and public benefit from better, more available and affordable nutritious services and products and 2) improving the potential for industry to invest in developing healthy and nutritious services and products that meet consumer needs. This could be a groundbreaking model for progress in improving the engagement of nutrition science community with businesses. Support or Funding Information Support is provided by the Canadian Government, Department of Foreign Affairs, Trade and Development (DFATD)
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,039 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,015 |
| Communication savante | 0,017 | 0,017 |
| Science ouverte | 0,005 | 0,014 |
| Intégrité de la recherche | 0,009 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».