Development of digital learning tools for medical education with agile scrum methodology
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has underscored the importance of digital learning tools in medical education. However, there is little evidence exploring how or whether such tools are being developed for family medicine curricula. We provide a narrative analysis of how the University of Ottawa's Department of Family Medicine (DFM) developed innovative learning tools using an interdisciplinary, research-based approach and Agile Scrum Methodology. METHODS: In March 2020, the DFM created an interdisciplinary team to support development of digital tools for medical education. Members of the DFM were invited to participate in the project during two faculty-wide webinars held on May 13, 2020. Participants identified three topic areas for which digital learning tools were to be created: Choosing Wisely Canada recommendations, Hypertension, and Quality Improvement (QI). Representatives from the Faculty of Engineering were recruited to support IT development for the tools, while researchers from the Bruyère Research Institute provided support for methodology and analysis. RESULTS: Three teams developed prototypes for digital learning tools: a "choose your own adventure" game to teach Choosing Wisely Canada criteria, an interactive hypertension clinic, and a "QI escape room" focused on quality improvement strategies. One team created a website to host learning tools, and the final team generated an evidence library for product development. CONCLUSION: Adhering to these methodologies helped us to manage interdisciplinary teams and support their success, with all five teams completing their objectives. The interdisciplinary and incremental approach of Scrum methodology allowed for gaps to be identified and addressed in real time. Scrum demonstrates promise and should receive further consideration as a method for developing learning tools in medical education.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".