Tool for assessing food industry commitments and practices to address the double burden of malnutrition: a Delphi study
Bibliographic record
Abstract
BACKGROUND: Many low- and middle-income countries face a double burden of malnutrition, i.e., a co-occurrence of undernutrition with overweight, obesity, or other diet-related noncommunicable diseases. In an increasingly connected global food system, multinational and domestic food industry actors - through their commercial practices and corporate political activity - both contribute to the double burden of malnutrition and hold potential to address it. Systematic monitoring of relevant industry commitments and practices may help to hold industry accountable and foster constructive engagement. The Business Impact Assessment - Obesity and population-level nutrition (BIA-Obesity) tool has been developed to assess and benchmark food companies' commitments and practices related to obesity and support for healthy diets at a national level. METHODS: To enable the application of BIA-Obesity for countries facing a double burden of malnutrition, this study aimed to identify and select relevant best practice indicators for assessing food company commitments and practices regarding the double burden of malnutrition, with a focus on indicators not currently captured by the BIA-Obesity tool. A three-round Delphi study was conducted between April and October 2024, involving an international panel of experts. RESULTS: From 52 invited experts, 30 contributed to our expert panel (response rate 58%). Based on a systematic review, 16 best practice indicators addressing the double burden of malnutrition were proposed. Consensus (i.e., group agreement of 75% or higher) for inclusion was reached for 8 indicators covering the production, distribution and marketing of (i) breastmilk substitutes and (ii) complementary foods, (iii) breastfeeding support and (iv) parental leave for employees, (v) food fortification, (vi) use of traditional foods, (vii) use of discounts and donations, and (viii) healthy diets at work. One additional indicator on corporate strategy was included as an overarching indicator. CONCLUSIONS: Food industry action may complement other efforts to address the double burden of malnutrition, such as public policies and investments. Tools like the extended BIA-Obesity framework can be used for a systematic monitoring of relevant industry commitments and practices and may help to disseminate and establish favourable industry practices as part of broader efforts to address the double burden of malnutrition in low- and middle-income countries. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.108 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".