Adopting and Implementing an Innovative Model to Organize Diabetes Care Within First Nations Communities: A Qualitative Assessment
Bibliographic record
Abstract
Abstract Background: Diabetes care remains suboptimal in First Nations populations. Innovative and culturally relevant approaches are needed to promote systematic and proactive organization of diabetes care for people living with diabetes on-reserve in Canada. The RADAR model is one strategy to improve care: an integrated disease registry paired with an electronic health record for local community healthcare providers with remote care coordination. We qualitatively assessed adoption and implementation of RADAR in First Nations communities in Alberta to inform its potential spread in the province. Methods: We used the RE-AIM framework to evaluate adoption and implementation of RADAR in 6 First Nations communities. Using purposeful sampling, we recruited local healthcare providers and remote care coordinators involved in delivering RADAR to participate in telephone or in-person interviews at 6- and 24-months post-implementation. Interviews were digitally recorded, transcribed, and verified for accuracy. Data was analyzed using content analysis and managed using ATLAS.ti 8.Results: In total, we conducted 21 semi-structured interviews (6 at 6-months; 15 at 24-months) with 11 participants. Participants included 3 care coordinators and 8 local healthcare providers, including registered nurses, licensed practical nurses, and registered dietitians. We found that adoption of RADAR was influenced by leadership as well as appropriateness, acceptability, and perceived value of the model. In addition, we found that implementation of RADAR was variable across communities regardless of implementation supports and appropriate community-specific adaptations.Conclusions: The variable adoption and implementation of RADAR has implications for how likely it will achieve its anticipated outcomes. RADAR is well positioned for spread through continued appropriate community-based adaptations and by expanding the existing implementation supports, including dedicated human resources to support the delivery of RADAR and the provision of levels of RADAR based on existing or developed capacity among local HCPs.Trial registration: Not applicable to this qualitative assessment.
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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 | 0.032 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".