The feasibility of multisectoral policy options aimed at reducing trans fats and encouraging its replacement with healthier oils in India
Bibliographic record
Abstract
INTRODUCTION: The World Health Organization recommends replacement of trans fat with polyunsaturated fat to reduce cardiovascular disease risk. Although several high-income countries have been successful in reducing trans fat in the food supply, low- and middle-income countries such as India may face additional contextual challenges such as the large informal sector, lack of consumer awareness, less enforcement capacity and low availability and affordability of healthier unsaturated fats. The objective of this study was to examine the feasibility and acceptability of multisectoral policy options aimed at supporting trans fat reduction and its replacement with polyunsaturated fats in India. METHODS: Multisectoral policy options examined in this study were identified using food supply chain analysis. Semi-structured interviews (n = 17) were conducted with key informants from agriculture, trade, finance, retail, industry, food standards, non-governmental organizations and the health professions to gain their views on the feasibility and acceptability of the policy options. Purposive sampling was used to identify key informants. Data were coded and organized based on key themes. RESULTS: There was support for policies aimed at improving the quality of seeds, supporting farmer co-operatives and developing affordable farming equipment suited to smallholders to improve the production of healthier oils. Increasing the role of the private sector to improve links among producers, processors and retailers may help to streamline the fats supply chain in India. Blending healthier oils with oils high in saturated fat, which are currently readily available, could help to improve the quality of fat in the short term. Improving consumer awareness through mass media campaigns and improved labelling may help increase consumer demand for healthier products. CONCLUSIONS: Reorienting agricultural policies to support production of healthier oils will help increase their uptake by industry. Policy coherence across sectors will be critical to reduce trans fat intakes and could be improved by increasing engagement among researchers, the private sector and government.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".