Hidden in Plain Sight: Finding a Balance Between Assessment and Learning in Competency-Based Education in Canadian Health Care
Bibliographic record
Abstract
Due to its emphasis on skill development and alignment with workforce demands, competency-based education (CBE) has garnered considerable attention in recent years. My organizational improvement plan (OIP) focuses on the potential benefits of incorporating learners’ voices into CBE in Canadian medical education and proposes a corresponding implementation framework. The traditional CBE model often lacks a critical component: the learner’s voice. My OIP reviews the literature and outlines its theoretical underpinnings (e.g., systems theory, adult education theory) within the scope of authentic leadership. The findings suggest incorporating learners’ voices into CBE to boost engagement, motivation, and agency. In response to such efforts, learners have reported feeling more connected to the learning process. Instructors have also reported that incorporating learners’ voices into their educational pedagogy helped them to tailor their instruction to learners’ needs. The proposed framework features four components: 1) listening to learners’ needs and concerns; 2) involving learners in the design of learning outcomes; 3) using learners’ feedback to adapt instruction; and 4) empowering learners to take ownership of their learning. This study highlights the importance of including learners’ voices in CBE to promote learner-centredness and enhance learning outcomes. The proposed framework offers a practical guide for CBE instructors to incorporate learners’ voices into their instruction. Most importantly, this study contributes to the ongoing discussion on improving CBE and creating more equitable and effective learning environments for all learners.
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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 | 0.035 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".