BMC medical education · 2025
Training clinicians to facilitate diabetes group visits.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ameri Moe, Jabbar Layla Abdul, Kim Stephanie, Raji Mukaila, Samson Susan L, Vaughan Elizabeth M
Аннотация
INTRODUCTION: Managing diabetes in community clinics often presents substantial challenges. Diabetes group visits, shared medical appointments involving a clinician, offer a potential solution to these challenges. However, there is a need for training clinicians to effectively lead and facilitate these group visits.
AIM: The aim of this study is to develop and assess a training program designed to equip clinician learners with the skills to facilitate diabetes group visits.
METHODS: Using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) as a guiding framework, we developed a 3-hour virtual training program for clinician learners at two community clinics. The training covered group visit facilitation and the logistics of 1:1 clinician-patient encounters. For the patient encounter component, we applied evidence-based guidelines, such as those from the American Diabetes Association, to create novel algorithms specifying low-cost medications for diabetes, hypertension, and hyperlipidemia. Training effectiveness was evaluated through learner participation (6-10 learners per site), knowledge improvement measured via pre- and post-tests, case studies, and learner feedback.
RESULTS: Clinics successfully met their clinician recruitment targets. Learners showed proficiency in applying the medication algorithms through five case studies. Knowledge improved significantly from the pretest (46.36%) to the posttest (92.95%) (p < 0.001). Learner feedback indicated high satisfaction with the training's structure, content, and relevance, particularly in relation to using the algorithms to manage diabetes in low-income settings.
DISCUSSION: This study demonstrates the successful development of a diabetes group visit training for clinicians, as evidenced by recruitment success, knowledge improvement, and positive feedback. The low-cost medication algorithms served as a valuable resource for clinicians.
CLINICAL TRIAL: NCT04835493.
AIM: The aim of this study is to develop and assess a training program designed to equip clinician learners with the skills to facilitate diabetes group visits.
METHODS: Using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) as a guiding framework, we developed a 3-hour virtual training program for clinician learners at two community clinics. The training covered group visit facilitation and the logistics of 1:1 clinician-patient encounters. For the patient encounter component, we applied evidence-based guidelines, such as those from the American Diabetes Association, to create novel algorithms specifying low-cost medications for diabetes, hypertension, and hyperlipidemia. Training effectiveness was evaluated through learner participation (6-10 learners per site), knowledge improvement measured via pre- and post-tests, case studies, and learner feedback.
RESULTS: Clinics successfully met their clinician recruitment targets. Learners showed proficiency in applying the medication algorithms through five case studies. Knowledge improved significantly from the pretest (46.36%) to the posttest (92.95%) (p < 0.001). Learner feedback indicated high satisfaction with the training's structure, content, and relevance, particularly in relation to using the algorithms to manage diabetes in low-income settings.
DISCUSSION: This study demonstrates the successful development of a diabetes group visit training for clinicians, as evidenced by recruitment success, knowledge improvement, and positive feedback. The low-cost medication algorithms served as a valuable resource for clinicians.
CLINICAL TRIAL: NCT04835493.