Cross-sectional examination of commercial milk formula industry funding of international, regional and national healthcare professional associations: protocol
Bibliographic record
Abstract
INTRODUCTION: Commercial milk formula manufacturers often emphasise their role in supporting infant and young child nutrition and breastfeeding, but their commercial goals to increase volume and profit margin of formula sales conflict with these declarations. Healthcare professional associations have an important role in healthcare worker education, shaping clinical practice. When healthcare professional associations enter into financial relationships with formula manufacturers, conflicts of interest arise, which may undermine education and practice that promotes optimal infant and young child feeding. The World Health Assembly calls on all parties to avoid such conflicts of interest, but it is uncertain how often this recommendation is followed. This protocol documents a systematic method to identify funding from the commercial milk formula industry among international, regional and national associations of healthcare professionals. METHODS AND ANALYSIS: Using systematic search strategies in the Gale Directory Library and Google, we will identify international healthcare professional associations relevant to maternal and child health. Data regarding funding relationships with the commercial milk formula industry over the past 24 months will be extracted from the official websites or, in their absence, social media accounts by two independent analysts. The analysis will focus on the presence of conflict of interest or sponsorship policies and type of funding, such as sponsorship or payment for services. ETHICS AND DISSEMINATION: This study does not require ethical approval and will use data available in the public domain. The results will be disseminated through peer-reviewed journal articles, at conferences and among the healthcare professional associations.
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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 | 0.050 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.013 |
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".