What Predicts Performance in Canadian Dental Schools?
Bibliographic record
Abstract
The task of selecting the best dental applicants out of an extremely competitive applicant pool is a problem faced annually by dental faculties. This study examined the validity of both cognitive and noncognitive factors used for selection to Canadian dental schools. Interest in personality measurement and the prediction offered by personality measures has escalated and may be applied to the selection of dental candidates. Therefore, the study also assessed whether the addition of a personality measure would increase the validity of predicting performance beyond that achieved by an interview and the Dental Aptitude Test. Results suggest that an interview may be useful in identifying specific behavioral characteristics deemed important for success in dental training. Consistent with previous research, results show that the Dental Aptitude Test is a good predictor of preclinical academic success, with prediction declining when clinical components of the program are introduced into the criterion. Results from the personality measure indicated that Openness to Experience was significantly related to aspects of clinical education, although, contrary to expectations, this relationship was negative. A facet of Openness, Ideas, together with Positive Emotions, a facet of Extroversion, improved prediction of performance in clinical studies beyond that provided by the Dental Aptitude Test and the Interview. Implications of the findings are discussed, and recommendations regarding the admission process to Canadian dental programs are offered.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".