A Novel Model of Pre‐competitive Public‐Private Collaboration for Nutrition Research for Vulnerable Populations
Bibliographic record
Abstract
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)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".