Successes and Challenges From a Motivational Interviewing-Informed Diabetes Prevention Program Situated in the Community
Bibliographic record
Abstract
To manage the rising prevalence of type 2 diabetes mellitus, sustainable diabetes prevention programs are needed. In this study, a process evaluation was conducted to qualitatively understand perceived successes and challenges of a diabetes prevention program situated in the community. This study took place in the first year of a multiyear project. Semistructured interviews were conducted with a sample of women clients (n = 14) postprogram and trainers (n = 10) 9 months into program implementation. Interviews were audio-recoded, transcribed verbatim, and analyzed using a Template Approach. Data were first analyzed deductively into two categories that aligned with the study's purpose (successes and challenges). Second, an inductive analysis was used to understand program delivery processes within each category. Clients and trainers expressed (a) program successes related to recruitment strategy, outlook on making behavior changes, and communication style used within the program and (b) program challenges surrounding effort of learning and applying the communication strategy, usefulness of program applications and tools, and program fit. This evaluation provides practical implications and future directions for diabetes prevention programs, and has informed tailoring and expansion of the program of focus. Results demonstrate the success of motivational interviewing from both client and trainer perspectives and the impact of community partnerships to increase prediabetes awareness in the community. Overall, the program's diabetes prevention and behavior change strategies coupled with a client-centered approach facilitated women clients in making diet and exercise modifications.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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 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".