Community Engagement in Kidney Research: Guatemalan Experience
Bibliographic record
Abstract
Abstract Background: Community engagement is essential for effective research when addressing issues important to the community and researchers. Despite its effectiveness, there is limited published evidence concerning the evaluation of community engagement in research projects, especially in the area of nephrology. Methods: We developed a community engagement program in Guatemala to address the role of hydration in chronic kidney disease of unknown origin, using five key engagement principles: 1. Local relevance and determinants of health. 2. Acknowledgment of the community. 3. Dissemination of findings and knowledge gained to all partners. 4. Usage of community partners’ input. 5. Involvement of a cyclical and iterative process in the pursuit of goals. The effectiveness of community engagement was measured by a structured questionnaire on a 5-point likert scale. This measure determined how well and how often the research team adhered to the five engagement principles. Results: Sixty-two community leaders completed the questionnaire. Seventy-five percent were female, with a mean age of 37 years. All 5 engagement principles scored highly on the 5-point likert scale. Conclusion: Community leaders and researchers developed a relationship in which commitment and empowerment facilitated the participation in all aspects of the research process. This program is a useful tool for researchers, especially in low-middle income countries, to start research in a 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".