Developing a Successful Implementation Plan for a High Frequency, Low Touch Care Model at Specialized Type 1 diabetes Clinics: The Type 1 Diabetes Virtual Self-Management and Education support (T1ME) Trial
Bibliographic record
Abstract
Abstract Objective To understand the provider- and system-level factors affecting successful implementation of a virtual care intervention into type 1 diabetes (T1D) clinics. Methods Semi-structured interviews with managers and certified diabetes educators (CDEs) at diabetes clinics across Southern Ontario, prior to the COVID-19 pandemic. Deductive analysis using the Theoretical Domains Framework, then mapping to Behaviour Change Techniques to inform potential implementation strategies for high frequency virtual care for T1D. Results There was considerable intention to deliver high frequency virtual care to patients with T1D. Participants believed that this model of care could lead to improved patient outcomes and engagement, but would likely increase the workload of CDEs. Some felt there were insufficient resources at their site to enable them to participate in the program. Member-checking conducted during the pandemic revealed that clinics and staff had already developed strategies to overcome resource barriers to the adoption of virtual care during the pandemic. Conclusion Existing enablers for high frequency virtual care can be leveraged, and barriers can be overcome with targeted clinical incentives and support.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".