Transitions in teaching: he experiences of basic scientists changing to an integrated anatomy curriculum (532.5)
Bibliographic record
Abstract
Objective: The purpose of this study was to understand what the experience of transitioning to an integrated curriculum is like for anatomy educators with a basic science background, and to interpret what it means for their teaching. Methods: Lillian Douglas School of Medicine (pseudonym) was used as a case study of an anatomy program that has successfully transitioned to teaching anatomy in a clinically relevant way. A phenomenological approach was used to explore what it was like for basic scientists to change to teaching in an integrated curriculum. The primary investigator spent one month with the program during which she interviewed participants and observed lectures and labs. Data was analyzed using the reiterative cycle of hermeneutics. Findings: Using Daniel Pratt’s General Model of Teaching as a conceptual framework, transitioning to an integrated curriculum entailed a shift in the elements of teaching from emphasis on the teacher and content to students and the context of clinical practice. The foregrounding of students and context included both a change in content in terms of clinical cases, and a change in teaching towards a more student centered “Socratic” approach. These changes involved collaborating with physicians and pedagogical experts respectively, as well as ceding control in terms of deciding what to include in the course and how they would go about teaching it. Grant Funding Source : Supported by the Social Sciences and Humanities Research Council of Canada
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".