Problem-based Learning Curriculum Disconnect on Diversity, Equity, and Inclusion
Bibliographic record
Abstract
ABSTRACT Background Diversity, equity, and inclusion (DEI) mission statements continue to be adopted by academic institutions in general, and by dental schools around the globe in particular. But DEI content seems to be under-developed in dental education. Objectives The objectives of this study were two-fold: to extract information from all the PBL cases at University of British Columbia’s Faculty of Dentistry curriculum in terms of the diversity, equity, and inclusion of patient and provider characteristics, context, and treatment outcomes; and; to compare these findings with the composition of the British Columbia census population, dental practice contextual factors, and the evidence on treatment outcomes within patient care. Methods Information from all the 58 PBL cases was extracted focusing on patient and provider characteristics (e.g., age, gender, ethnicity), context (e.g., type of insurance), and treatment outcomes (e.g., successful/unsuccessful). This information was compared with the available literature. Results From all the 58 PBL cases, 0.4% included non-straight patients, while at least 4% of BC residents self-identify as non-straight; there were no cases involving First Nations patients although they make up 6% of the British Columbia population. Less than 10% of the cases involved older adults who make up almost 20% of the population. Only Treatments involving patients without a disability were 5.74 times more likely to be successful compared to those involving patients with a disability ( p <0.05). Conclusions The characteristics of the patients, practice context, and treatment outcomes portrayed in the existing PBL cases seem to differ from what is known about the composition of the British Columbia population, treatment outcome success, and practice context; a curriculum disconnect seems to exist. The PBL cases should be revised to better represent the population within which most students will 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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".