Implementation of a diabetes prevention program within two community sites: a qualitative assessment
Bibliographic record
Abstract
BACKGROUND: Despite numerous translations of diabetes prevention programs, implementation evaluations are rarely conducted. The purpose of this study was to examine the implementation process and multilevel contextual factors as an evidence-based diabetes prevention program was implemented into two local community organization sites to inform future scale-up. To build the science of implementation, context and strategies must be identified and explored to understand their impact. METHODS: The program was a brief-counseling diet and exercise modification program for individuals at risk of developing type 2 diabetes. A 1-year collaborative planning process with a local not-for-profit community organization co-developed an implementation plan to translate the program. A pragmatic epistemology guided this research. Semi-structured interviews were conducted with staff who delivered the program (n = 8), and a focus group was completed with implementation support staff (n = 5) at both community sites. Interviews were transcribed verbatim and thematically analyzed using a template approach. The consolidated framework for implementation research (CFIR) is a well-researched multilevel implementation determinant framework and was used to guide the analysis of this study. Within the template approach, salient themes were first inductively identified, then identified themes were deductively linked to CFIR constructs. RESULTS: Implementation strategies used were appropriate, well-received, and promoted effective implementation. The implementation plan had an impact on multiple levels as several CFIR constructs were identified from all five domains of the framework: (a) process, (b) intervention characteristics, (c) outer setting, (d) inner setting, and (e) individual characteristics. Specifically, results revealed the collaborative 1-year planning process, program components and structure, level of support, and synergy between program and context were important factors in the implementation. CONCLUSION: This study offers insights into the process of implementing a community-based diabetes prevention program in two local sites. Successful implementation benefited from a fully engaged, partnered approach to planning, and subsequently executing, an implementation effort. The CFIR was a useful and thorough framework to evaluate and identify multilevel contextual factors impacting implementation. Results can be used to inform future implementation and scale-up efforts.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".