Evolving Curriculum Design: A Novel Framework for Continuous, Timely, and Relevant Curriculum Adaptation in Faculty Development
Bibliographic record
Abstract
The time lag between needs assessment and implementation of faculty development curricula assumes a certain stability of participants' individual and contextual needs which may not reflect the often complex and shifting priorities in health professional schools. In addition to the variability of issues they face, participants are typically better able to recognize and articulate their needs once engaged in a curriculum.This article is a conceptual description of how applying an umbrella strategy to curriculum design illuminated an iterative methodology for continuous adaptation of the 2004-2006 University of Toronto Education Scholars Program in real time to the emergent needs of participants and their context. The general goals or umbrella for the core curriculum were determined by a broad-based environmental scan. In keeping with a learner-centered collaborative program, a number of process strategies were developed to solicit input from participants during the two years of the program. These included creating a dialogue space, use of class and program evaluations, modified Delphi needs assessments, and opinion leader interviews. Adaptation of curriculum was enabled by protection of curriculum time and flexibility of course leadership. The application of strategy theory to curriculum design has not been previously described. This iterative approach enabled course leadership to successfully identify multiple unperceived issues to address. With this unique and cyclical process, curricular relevance and timeliness are ensured as well as enhancing participant motivation and engagement, consistent with adult learning principles. This methodology should be considered by course directors of all continuing professional development programs.
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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 | 0.051 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".