Deliberative curriculum inquiry for integration in an MD curriculum: Dalhousie University's curriculum renewal process
Bibliographic record
Abstract
BACKGROUND: Dalhousie University's MD Programme faced a one-year timeline for renewal of its undergraduate curriculum. AIM: Key goals were renewed faculty engagement for ongoing quality improvement and increased collaboration across disciplines for an integrated curriculum, with the goal of preparing physicians for practice in the twenty-first century. METHODS: We engaged approximately 600 faculty members, students, staff and stakeholders external to the faculty of medicine in a process described by Harris (1993) as 'deliberative curriculum inquiry'. Temporally overlapping and networked intraprofessional and interprofessional teams developed programme outcomes, completed environment scans of emerging content and best practices, and designed curricular units. RESULTS: The resulting curriculum is the product of new collaborations among faculty and exemplifies distinct forms of integration. Innovations include content and cases shared by concurrent units, foundations courses at the beginning of each year and integrative experiences at the end, and an interprofessional community health mentors programme. CONCLUSION: The use of deliberative inquiry for pre-med curriculum renewal on a one-year time frame is feasible, in part through the use of technology. Ongoing structures for integration remain challenging. Although faculty collaboration fosters integration, a learner-centred lens must guide its design.
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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 | 0.057 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| 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".