Who Is Teaching What, When? An Evolving Online Tool to Manage Dental Curricula
Bibliographic record
Abstract
There are numerous issues in the documentation and ongoing development of health professions curricula. It seems that curriculum information falls quickly out of date between accreditation cycles, while students and faculty members struggle in the meantime with the "hidden curriculum" and unintended redundancies and gaps. Beyond knowing what is in the curriculum lies the frustration of timetabling learning in a transparent way while allowing for on-the-fly changes and improvements. The University of British Columbia Faculty of Dentistry set out to develop a curriculum database to answer the simple but challenging question "who is teaching what, when?" That tool, dubbed "OSCAR," has evolved to not only document the dental curriculum, but as a shared instrument that also holds the curricula and scheduling detail of the dental hygiene degree and clinical graduate programs. In addition to providing documentation ranging from reports for accreditation to daily information critical to faculty administrators and staff, OSCAR provides faculty and students with individual timetables and pushes updates via text, email, and calendar changes. It incorporates reminders and session resources for students and can be updated by both faculty members and staff. OSCAR has evolved into an essential tool for tracking, scheduling, and improving the school's curricula.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".