Factors Impacting the Uptake of Research into Dietary Sodium Reduction Policies in Five Latin American Countries: A Qualitative Study
Bibliographic record
Abstract
Diets high in sodium are a risk factor for cardiovascular disease (CVD). Latin American countries (LAC) consume more than double the recommended sodium levels. Research uptake in dietary sodium reduction policies has been inconsistent in LAC, and the factors impacting research uptake are largely unknown. This study aimed to describe the barriers and facilitators to the uptake of research into sodium reduction policies from a funded research consortium with 5 LAC (Argentina, Brazil, Costa Rica, Paraguay, and Peru). A qualitative case study included 5 researchers and 4 Ministry of Health officers from the funded consortium. Dimensions from Trostle’s framework of actors, content, context, and process and relative advantages from the Diffusion of Innovation informed the semi-structured interview guide and analysis. One-on-one interviews were completed from November 2019 to January 2020. The participants validated transcripts, coded, and analyzed using NVivo software. Key barriers to policy advancements included 1) conflicts of interest from the food industry and some government actors; 2) government turnover resulting in policy and personnel changes; 3) a lack of human and financial resources; and 4) and communication gaps among key actors. Key facilitators to policy advancement included: 1) the content and quality of health economic, food supply, and qualitative data; 2) support, technical assistance, and alliances with the government, non-governmental organizations, and international experts; and 3) researchers enhanced skillsets facilitated with communication and dissemination with policymakers. Researchers and policymakers are faced with several barriers and facilitators on research uptake in policies and programs in LAC; these factors should be addressed and leveraged to advance sodium reduction policy development. Future LAC studies can draw from the insights and lessons learned from this case study and apply the results to future efforts on policy nutrition to promote healthy eating and reduce CVD risk.
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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 | 0.057 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".