Design thinking sprints as a facilitation process to enact change in the residency match process and beyond
Bibliographic record
Abstract
Implication Statement: Enacting change in medical education requires effective facilitation processes. Medical education lags behind other fields in systems innovation and radically disruptive approaches to the challenges we encounter. Design thinking "sprints," widely used in many other settings, serve as an opportunity to fill the gap as a facilitation process during periods requiring extensive and/or rapid change. Though resource-intensive, our experience using design thinking sprints for a situation requiring urgent change management with high-stakes implications for Canadian medical education to demonstrate their utility. A more widespread, adoption can contribute to innovation within all aspects of education including curriculum design, policy development, and educational process renewal. Énoncé des implications de la recherche: dans une situation nécessitant une gestion urgente de changements à enjeux importants pour l'éducation médicale au Canada démontre son utilité, malgré les ressources considérables qui ont dû être mobilisées. Une adoption plus large de cette approche peut contribuer à l'innovation dans tous les aspects de l'éducation, y compris la conception des programmes d'études, l'élaboration de politiques et le renouvellement des processus éducatifs.
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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 | 0.056 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".