Community engagement in kidney research: Guatemalan experience
Bibliographic record
Abstract
BACKGROUND: Community engagement is essential for effective research when addressing issues important to both 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. We assessed internal consistency for each set of the engagement items through Omega coefficient. 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. Every item set corresponding to an engagement principles evaluation had a Omega coefficient > 0.80, indicating a firm internal consistency for all question groups on both qualitative and quantitative scales. CONCLUSION: Engagement of the community in the kidney research provides sustainability of the efforts and facilitates the achievements of the goals. Community leaders and researchers became a team and develop a relationship in which commitment and empowerment facilitated the participation in all aspects of the research process. This initiative could be a useful tool for researchers, especially in low-middle income countries, to start research in a community, achieve objectives in a viable form, and open opportunities to further studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".