Implementation Plan for a High-Frequency, Low-Touch Care Model at Specialized Type 1 Diabetes Clinics: Model Development
Bibliographic record
Abstract
BACKGROUND: Individuals with type 1 diabetes (T1D) are more likely to achieve optimal glycemic management when they have frequent visits with their health care team. There is a potential benefit of frequent, telemedicine interventions as an effective strategy to lower hemoglobin A1c (HbA1c). OBJECTIVE: The objective is this study was to understand the provider- and system-level factors affecting the successful implementation of a virtual care intervention in type 1 diabetes (T1D) clinics. METHODS: Semistructured interviews were conducted with managers and certified diabetes educators (CDEs) at diabetes clinics across Southern Ontario before the COVID-19 pandemic. Deductive analysis was carried out using the Theoretical Domains Framework, followed by mapping to behavior 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. CONCLUSIONS: Existing enablers for high-frequency virtual care for T1D can be leveraged, and barriers can be overcome with targeted clinical incentives and support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".