A Team-Based Training for Continuous Glucose Monitoring in Diabetes Care: Mixed Methods Pilot Implementation Study in Primary Care Practices
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».