Consulting report - Municipality of Surco: social project proposal to reduce childhood obesity of primary education students in a public school in the District of Santiago de Surco
Bibliographic record
Abstract
This thesis addresses the increasing prevalence of childhood obesity and overweight in the district of Santiago de Surco, Lima, Peru, with a focus on primary school children aged 6 to 11 years. By using the Design Thinking Methodology, the study analyzes the key factors contributing to this public health issue, such as limited access to nutritious food and insufficient physical activity. The thesis presents a proposed solution through a social program that provide free after-school physical activities, in partnership with the Municipality of Santiago de Surco and Smart Fit. Its goal is to increase the number of hours of exercise that children participate in on a weekly basis, thereby addressing on of the main contributors of the childhood obesity. The study also evaluates the scalability of the program, considering its potential expansion to other age groups, schools, and districts. Additionally, the program emphasizes the importance of the community collaboration, including the parents and local businesses, to ensure the active participation and the initiative’s financial sustainability. By promoting an active lifestyle and health habits of children, the thesis contributes to combat childhood obesity in Peru, aligning with the country’s Sustainable Development Goals 2 and 3.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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".