Community Health Ambassadors
Bibliographic record
Abstract
Despite public health efforts to address burden of diseases within communities such as diabetes, health disparities remain. Traditional lay health advisor models help address these issues. Yet, few, if any, have a statewide focus that includes education credit and involves broad-based partnerships. The Community Health Ambassadors Program (CHAP) is a training and education demonstration program designed to engage leaders from diverse communities to help eliminate health disparities in North Carolina. The program's current focus is on improving diabetes awareness, management, and prevention. CHAP involves multiple state and local community and healthcare professional partnerships, the community college system, and tribal, community-, and faith-based organizations. CHAP components include recruitment, training (classroom and interactive instruction, fieldwork, and continuing education credits), monitoring/evaluation, and support/education. Since CHAP's inception in June 2006, 146 community health ambassadors (CHAs) from 17 counties have been trained. Preliminary evaluation of the CHA community activities include one-on-one diabetes self-management tips, diabetes talks, and recruitment of citizens to sign healthy living pledges. CHAP may be a comprehensive and cost-effective model for promoting multilevel involvement of community leaders and diverse organizations to concentrate on diabetes health disparities within the state. CHAP will be implemented in the future to address the state's other prevailing health problems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.256 | 0.049 |
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".