In the Students ’ Own Words: What Are the Strengths and Weaknesses of the Dental
Bibliographic record
Abstract
Abstract: Dental students have little input into the selection of course topics and subject matter included in their dental curricula. Curriculum requirements are framed by the Commission on Dental Accreditation, which has stipulated competencies and associ-ated biomedical and clinical knowledge that must be addressed during dental school. Although these competency requirements restrict the variance of educational experiences, students are eager to share their views on the curriculum within the realm of their educational experience. The objective of this research project was to elicit the perspectives of dental students from a broad cross-section of U.S. and Canadian dental schools about their education. A total of 605 students (285 sophomores, 220 seniors, 100 residents) from twenty North American dental schools completed a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis to communicate their perceptions of the curriculum. Students were also asked to provide their impressions of the overall quality of the educational program in an open-ended written format. The students ’ qualitative comments were then reviewed and categorized into key issues or themes. Resulting themes for each category of the Curriculum SWOT (C-SWOT) analysis were the following. Strengths: 1) clinical learning experience, and 2) opportunity to work with knowledgeable faculty. Weaknesses: 1) disorganized and inefficient clinical learning environment, 2) teaching and testing that focus on memorization, 3) poor quality instruction characterized by curricular disorganization, and 4) inconsistency among instructors during student evaluations. Op-portunities: 1) develop strategies to provide students with more exposure to patients, especially early in the curriculum, and 2) opportunities to learn new technology/techniques. Threats: 1) cost of dental education, 2) students ’ concerns about faculty “brain
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".