Designing Relevant and Authentic Scenarios for Learning Clinical Communication in Dentistry Using the Calgary-Cambridge Approach
Bibliographic record
Abstract
A clinical communication curriculum based on the principles of the Calgary-Cambridge approach was developed during the revision of the 5-year Bachelor of Dental Surgery program (BDS) at The University of Adelaide, Australia. To provide experiential learning opportunities, a simulated patient (SP) program using clinical scenarios was developed. We aimed to design the scenarios to reflect communication demands that student clinicians commonly encounter, that integrated process and content, and which students would perceive as authentic and relevant. Scenarios were based on data from focus groups with recent graduates and interviews with clinic tutors. The scenarios combined content (e.g. medical history) and process (e.g. questioning and relationship skills) at a level suitable for junior students. Students evaluated scenario-based materials and SP activities in a survey comprising Likert-scale and open-ended questions. Students rated the materials and SP activities positively; open-ended comments supported the ratings. Scenario-based materials and activities based on student-clinicians’ experiences, were perceived as relevant, realistic, and useful for learning. A curriculum designed on Calgary-Cambridge principles helped address student learning needs at particular stages of their program.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".