A Curriculum History of Postgraduate Medical Education in Canada, 1990-2020
Bibliographic record
Abstract
Curricular reforms and change initiatives in PGME are often reactive responses to global or local crises, risks, and opportunities. This reality renders our understanding of PGME curriculum incomplete. To better design and understand curricular change in a complex adaptive human system such as PGME, it is necessary to “think globally, act locally,” concomitantly looking back while thinking forward. Since 1990, PGME curricula in Canada has undergone many reform iterations including the EFPO project, the CanMEDS Roles framework, the CBME/CBD initiative, and the recent emergency response to the ongoing COVID-19 pandemic. To unpack these curricular transformations, contextualize drivers behind change, and examine change leadership, this multi-manuscript dissertation employed a curriculum-history approach using Braudel’s (1960) interdisciplinary Longue Durée framework in an exploratory sequential mixed methods design. This dissertation contributes to the body of knowledge in PGME by documenting national and international social, cultural, political, and ideological configurations and lived experiences that shaped PGME curricula between 1990 and 2020, providing an understanding of how the current system of training residents has evolved in Canada, and why PGME curricula is the way it is today. In Study 1, Skinner's (2002) hermeneutic approach and theory of intentionality were utilized to thematically analyze archival collections going back to 1970. In Study 2, Arksey’s and O’Malley’s (2005) framework for scoping reviews was employed to map the literature relevant to the CBME paradigmatic shift in Canada since 1990. In Study 3, a modified Delphi study of three rounds was conducted to gain consensus from 30 pan-Canadian CBME experts about the role of change leadership and driving forces behind curricular changes in PGME over the last 30 years. The findings of the three studies suggest that change in PGME is a learning process involving stakeholders’ co-creation, contextualization, and actualization of curricular change to meet the needs of local population served. The resultant GLOCALS model calls for a distributed and participatory change leadership approach, as well as an inclusive and engaging PGME governance at the global and local levels to guide successful curricular reforms. The results further recommend the addition of digital health literacy and equity, diversity, inclusion, and indigeneity (EDII) to the CanMEDS-2025 version.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".