Reaching Consensus on Essential Biomedical Science Learning Objectives in a Dental Curriculum
Bibliographic record
Abstract
This article describes how the University of British Columbia Faculty of Dentistry reached consensus on essential basic biomedical science objectives for DMD students and applied the information to the renewal of its DMD curriculum. The Delphi Method was used to build consensus among dental faculty members and students regarding the relevance of over 1,500 existing biomedical science objectives. Volunteer panels of at least three faculty members (a basic scientist, a general dentist, and a dental specialist) and a fourth-year dental student were formed for each of 13 biomedical courses in the first two years of the program. Panel members worked independently and anonymously, rating each course objective as "need to know," "nice to know," "irrelevant," or "don't know." Panel members were advised after each round which objectives had not yet achieved a 75% consensus and were asked to reconsider their ratings. After a maximum of three rounds to reach consensus, a second group of faculty experts reviewed and refined the results to establish the biomedical science objectives for the renewed curriculum. There was consensus on 46% of the learning objectives after round one, 80% after round two, and 95% after round three. The second expert group addressed any remaining objectives as part of its review process. Only 47% of previous biomedical science course objectives were judged to be essential or "need to know" for the general dentist. The consensus reached by participants in the Delphi Method panels and a second group of faculty experts led to a streamlined, better integrated DMD curriculum to prepare graduates for future practice.
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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 | 0.280 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".