Designing and Developing Online Training for Diabetes Prevention Program Coaches Using an Integrated Knowledge Translation Approach: Development and Usability Study
Bibliographic record
Abstract
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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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".