A Team-Based Training for Continuous Glucose Monitoring in Diabetes Care: Mixed Methods Pilot Implementation Study in Primary Care Practices
Bibliographic record
Abstract
BACKGROUND: The American Academy of Family Physicians (AAFP) develops and maintains continuing medical education that is relevant to modern primary care practices. One continuing medical education modality is AAFP TIPS, which are comprised of resources designed for family medicine physicians and their care teams that aid in quick and accessible practice improvement strategies, with actionable steps. Evaluating physicians' use of and satisfaction with this modality's content and implementation strategies has not been prioritized previously. Continuous glucose monitoring (CGM) plays an increasing role in the treatment of diabetes; uptake occurs more rapidly in endocrinology settings than in primary care settings. To help address such differences in CGM uptake and diabetes care, AAFP TIPS on Continuous Glucose Monitoring (AAFP TIPS CGM) was developed, using published evidence and input from content experts (family medicine faculty; AAFP staff; and an advisory group comprised of other primary care physicians, patients, a pharmacist, and a primary care practice facilitator). A pilot implementation project was conducted in 3 primary care practices. OBJECTIVE: To evaluate AAFP TIPS CGM in primary care practices, the research team assessed use of and satisfaction with the content and assessed barriers to and facilitators for strategy and workflow implementation. METHODS: In total, 3 primary care practices participated in a mixed methods pilot implementation of AAFP TIPS CGM between June and October 2021. Practice champions at each site completed AAFP TIPS CGM and baseline practice surveys to evaluate practice characteristics and CGM prescribing. They conducted team trainings (via webinars or in person), with the goals of implementing CGM into practice and establishing or improving CGM workflows. Practice champions and team training participants completed posttraining surveys to evaluate the training, AAFP TIPS materials, and likelihood of implementing CGM. Interviews were conducted with 6 physicians, including practice champions, 2 months after team training. Satisfaction surveys were also distributed to those who completed the AAFP TIPS CGM course via the internet during the study period. RESULTS: Of the 3 practices, 2 conducted team trainings. The team training evaluation survey showed that practice staff understood their role in implementing CGM in practice (19/20, 95%), and most (11/20, 55%) did not have questions after the training. Insurance coverage for CGM was a remaining knowledge gap and potential barrier to implementing CGM in practice. Physicians and interdisciplinary care team members who took the AAFP TIPS CGM course via the internet, as well as those who attended in-person team training, expressed a high degree of satisfaction with the education, content, and applicability of the course. CONCLUSIONS: This pilot implementation of AAFP TIPS CGM offers pertinent and timely information for primary care practices that desire to initiate or expand CGM use to best meet the needs of their patients with diabetes.
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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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".