Fixing disconnects: Exploring the emergence of principled adaptations in a competency‐based curriculum
Bibliographic record
Abstract
PURPOSE: Competency-based medical education (CBME) promises to improve medical education through curricular reforms to support learner development. This intention may be at risk in the case of a Canadian approach to CBME called Competence by Design (CBD), since there have been negative impacts on residents. According to Joseph Schwab, teachers, learners and milieu must be included in the process of curriculum-making to prevent misalignments between intended values and practice. This study considered what can be learned from the process of designing, enacting and adapting CBD to better support learners. METHODS: This qualitative study explored the making of CBD through the perspectives of implementation leads (N = 18) at national, institutional and programme levels. A sociomaterial orientation to agency in curriculum-making guided the inductive approach to interviewing and analysis in phase one. A deductive analysis in phase two applied Schwab's theory to further understand sources of misalignments and the purpose of adaptive responses. RESULTS: Misalignments occurred when the needs of teachers, learners and milieu were initially underestimated in the process of curriculum-making, disconnecting assessment practices from experiences of teaching, learning and entrustment. While technical and structural issues posed significant constraints on agency, some implementation leads were able to make changes to the curriculum or context to fix the disconnects. We identified six purposes for principled adaptations to align with CBME values of responsive teaching, individualised learning and meaningful entrustment. CONCLUSION: Collectively, the adaptations we characterise demonstrate constructive alignment, a foundational principle of CBME in which assessment and teaching work together to support learning. This study proposes a model for making context-shaped, values-based adaptations to CBME to achieve its promise.
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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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".