Assessing equity in access to healthy diets in Ecuador following the addition of food sovereignty to the constitution
Bibliographic record
Abstract
Background: Ecuador shows high and increasing rates of diet-related non-communicable diseases, attributed in part to a nutrition transition toward more animal-based and processed foods. In 2008, Ecuador introduced the right to food sovereignty to its constitution in an effort to improve diets and protect local agricultural production. However, this has not yet translated to evidence of improved nutrition at the community level. Objective: This thesis examines whether the promotion of food sovereignty has contributed to improving access to healthy diets for marginalized populations in Ecuador; if so, it asks how and to what extent, and if not, it explores the barriers to achieving change and opportunities for improvement. This project thus seeks to provide suggestions of entry points for policies and programs to improve access to and consumption of healthy foods. Methods: Complementary qualitative methods were used to examine geographic access, food prices, nutritional knowledge and dietary preferences, and priorities for food policy improvement in three low-income neighbourhoods in the city of Machala, El Oro. Results: Access to affordable healthy foods is still an issue as perceived by the study neighbourhoods. Poor nutritional knowledge, high relative cost of fruits and vegetables, and inequitable geographic access to affordable healthy foods were the main barriers to healthy eating. Price was the primary factor influencing food purchasing and consumption behaviours. Knowledge of the concept of food sovereignty and its inclusion in the constitution was nonexistent, as was the awareness of any new policies or programs implemented to improve access to healthy foods since 2008. Conclusions: As there are no food sovereignty policies in place so far that address price, the affordability of healthy foods could be addressed either by improving the linkages between producers and consumers to reduce intermediaries, or by adopting fiscal policies that subsidize healthy foods and tax unhealthy foods to help make healthy options more affordable and viable. These policy initiatives fall within the potential scope of a commitment to food sovereignty, but greater focus is needed as the government advances in the development of specific policies and programs in order to have an impact on population health.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".