Multidisciplinary and participatory workshops with stakeholders in a community of extreme poverty in the Peruvian Amazon: Development of priority concerns and potential health, nutrition and education interventions
Bibliographic record
Abstract
BACKGROUND: Communities of extreme poverty suffer disproportionately from a wide range of adverse outcomes, but are often neglected or underserved by organized services and research attention. In order to target the first Millennium Development Goal of eradicating extreme poverty, thereby reducing health inequalities, participatory research in these communities is needed. Therefore, the purpose of this study was to determine the priority problems and respective potential cost-effective interventions in Belen, a community of extreme poverty in the Peruvian Amazon, using a multidisciplinary and participatory focus. METHODS: Two multidisciplinary and participatory workshops were conducted with important stakeholders from government, non-government and community organizations, national institutes and academic institutions. In Workshop 1, participants prioritized the main health and health-related problems in the community of Belen. Problem trees were developed to show perceived causes and effects for the top six problems. In Workshop 2, following presentations describing data from recently completed field research in school and household populations of Belen, participants listed potential interventions for the priority problems, including associated barriers, enabling factors, costs and benefits. RESULTS: The top ten priority problems in Belen were identified as: 1) infant malnutrition; 2) adolescent pregnancy; 3) diarrhoea; 4) anaemia; 5) parasites; 6) lack of basic sanitation; 7) low level of education; 8) sexually transmitted diseases; 9) domestic violence; and 10) delayed school entry. Causes and effects for the top six problems, proposed interventions, and factors relating to the implementation of interventions were multidisciplinary in nature and included health, nutrition, education, social and environmental issues. CONCLUSION: The two workshops provided valuable insight into the main health and health-related problems facing the community of Belen. The participatory focus of the workshops ensured the active involvement of important stakeholders from Belen. Based on the results of the workshops, effective and essential interventions are now being planned which will contribute to reducing health inequalities in the community.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".