Designing and Developing Online Training for Diabetes Prevention Program Coaches Using an Integrated Knowledge Translation Approach: Development and Usability Study
Notice bibliographique
Résumé
BACKGROUND: e-Learning has rapidly become a popular alternative to in-person learning due to its flexibility, convenience, and wide reach. Using a systematic and partnered process to transfer in-person training to an e-learning platform helps to ensure the training will be effective and acceptable to learners. OBJECTIVE: This study aimed to develop an e-learning platform for Small Steps for Big Changes (SSBC) type 2 diabetes prevention program coaches to improve the viability of coach training. METHODS: An integrated knowledge translation approach was used in the first 3 stages of the technology-enhanced learning (TEL) evaluation framework to address the study objective. This included three steps: (1) conducting a needs analysis based on focus groups with previously trained SSBC coaches, meetings with the SSBC research team, and a review of research results on the effectiveness of the previous in-person version of the training; (2) documenting processes and decisions in the design and development of the e-learning training platform; and (3) performing usability testing. Previously trained SSBC coaches and the SSBC research team were included in all stages of this study. RESULTS: Step 1 identified components from the in-person training that should be maintained in the e-learning training (ie, a focus on motivational interviewing), additional components to be added to the e-learning training (ie, how to deliver culturally safe and inclusive care), and mode of delivery (videos and opportunities to synchronously practice skills). Step 2 documented the processes and decisions made in the design and development of the e-learning training, including the resources (ie, time and finances) used, the content of the training modules, and how coaches would flow through the training process. The design and development process consisted of creating a blueprint of the training. The training included 7 e-learning modules, the learning modalities of which included narrated demonstration videos and user-engaging activities, a mock session with feedback from the research team, and a final knowledge test. Step 3, usability testing, demonstrated high levels of learnability, efficiency, memorability, and satisfaction, with minor bugs documented and resolved. CONCLUSIONS: Using an integrated knowledge translation approach to the technology-enhanced learning evaluation framework was successful in developing an e-learning training platform for SSBC coaches. Incorporating end users in this process can increase the chances that the e-learning training platform is usable, engaging, and acceptable. Future research will include examining the satisfaction of coaches using the SSBC coach e-learning training platform, assessing coach learning outcomes (ie, knowledge and behavior), and estimating the cost and viability of implementing this training.
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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 | 0,024 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».