Faculty and Student Perceptions of the Success of a Hybrid‐PBL Dental Curriculum in Achieving Curriculum Reform Benchmarks
Bibliographic record
Abstract
The dental education literature identifies eleven benchmark reform agenda curriculum qualities. The purpose of this study was to determine the extent to which the University of British Columbia D.M.D. curriculum was perceived by students and faculty as achieving these benchmarks and to note any differences in perceptions within and between the student and faculty groups. A WebEval survey consisting of twenty-one questions was delivered online in November 2007 to faculty members and D.M.D. students. The response rate was similar (~60 percent) for both students and faculty members. Comparisons were made between faculty members and students as well as within each group. For the faculty, we looked at the influence of appointment, focus, and teaching experience. For students, we looked at the influence of the year in the program, gender, and program track. Some differences (p<0.05) were identified within the faculty and student groups; however, there were many more differences between the faculty and the students, especially in areas related to curriculum redesign, collaborations with other health professions, preparation for independent practice, and creating a trust-based clinic environment. Faculty members were more optimistic about curriculum progress than were students. Improved communication of curriculum goals and explicit efforts at creating a safe and supportive learning environment could diminish these differences over time.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".