Randomized community trial to assess nutritional, socioeconomic, and health outcomes of a food forest initiative in Santa Elena Province, Ecuador: a study protocol
Bibliographic record
Abstract
Malnutrition is an escalating concern in low-and-middle-income countries (LMICs), including Ecuador, particularly within rural settings. To address this issue, food forests emerge as a promising intervention. This research protocol outlines a controlled intervention in the province of Santa Elena, aiming to evaluate the efficacy of a food forest in enhancing nutritional outcomes, with potential implications for broader replication. The study will be conducted in the Colonche Parish of Santa Elena Canton, where one commune will be randomly selected to receive the food forest intervention. In contrast, another similarly characterized commune, also randomly selected through cluster-based sampling, will serve as a control group, receiving no intervention. This randomized, comparative approach will enable a more precise assessment of the food forest's impact. Data collection will occur at three intervals: baseline, 6 months, and 12 months post-intervention. Comprehensive questionnaires will be employed to measure the food forest's influence on the communities' nutritional, economic, and health metrics, distinguishing between the intervention and control communes to elucidate the intervention's specific effects.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".