Transforming Community Members Into Diabetes Cultural Health Brokers
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate a community-based diabetes education pilot project. The Neighborhood Health Talker project aimed to train and implement cultural health brokers primarily targeting communities of color to improve community members' diabetes knowledge and diabetes self-management skills. A secondary aim was to establish diabetes resource libraries accessible to communities that normally experience barriers to these resources. METHODS: Recruited community members completed 1 week of formal training developed by a multidisciplinary team in Buffalo, NY. The effect of training was evaluated through the use of baseline surveys, a pretest/posttest covering all training content, and daily quizzes evaluating knowledge relevant to each of the five training modules. Trained NHTs then held at least five community conversations in various locations and administered anonymous postconversation surveys to participants. Descriptive statistics and qualitative analysis techniques were used to summarize test, quiz, and survey results. RESULTS: Twelve women and 1 man completed the training program. Working alone as well as in pairs, each held at least five community conversations reaching over 700 community members of all ages over 3 months and established 8 diabetes resource libraries in the community. All trainees increased their diabetes knowledge and confidence as well as their abilities to perform the tasks of a cultural health broker. Trainees also indicated that the goals they set at training initiation were met. CONCLUSIONS: The training was successful in increasing trainee knowledge and confidence about diabetes prevention and self-management. Participants not only developed proficiency in discussing diabetes, they also made important lifestyle changes that demonstrated their commitment to the cause and the project. Low-cost initiatives like this are easily reproducible in other communities of color and could be modified to meet the needs of other communities as well.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".