Implementing Specialized Diabetes Teams in Primary Care in Southern Ontario
Bibliographic record
Abstract
OBJECTIVES: This study explores the implementation processes of integrating specialized diabetes teams into primary care in southern Ontario, Canada. METHODS: In-depth qualitative interviews were conducted with 23 patients, 20 diabetes educators and 16 primary care physicians. In addition, group debriefing sessions were conducted and field notes were collected from diabetes educators and diabetes education program managers to further explore the day-to-day issues of implementation. Data were analyzed using an inductive content analysis approach. RESULTS: Analysis revealed 3 main themes: Right Place, Right Time, Right Service: the convenience and comfort of local care, timely, preventive management and delivering person-centred care; Creating Partnerships: generating intervention buy-in, formal discussion, service agreements, site orientation and team development; Operational Complexities and Strategies: access to electronic medical records and documentation, referral and scheduling procedures, and costs and resources. CONCLUSIONS: Because situating diabetes teams in primary care currently involves using existing healthcare structures and human resources, pragmatic methods of fostering successful implementation of this model of practice are required. The utility of this model was perceived as being viable, and benefits were visible to all study participants. Strategies to facilitate implementation include outlining roles and expectations by educators and the primary care providers' team in the beginning, investment in the intervention by all stakeholders, and clear channels of communication that allow educators to perform their roles and leverage opportunities for team collaboration in patient care. Further evaluation of implementation processes can serve to expand this model of practice, which has proven so far to be favourable to the players involved.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".