Do Factors from Admissions and Dental School Predict Performance on National Board Exams? A Multilevel Modeling Study
Bibliographic record
Abstract
The aim of this study was to assess the association among admissions variables, dental school performance, and licensing exam performance for six cohorts of graduates of one dental school. Data from all dental school graduates of Schulich School of Medicine & Dentistry, Western University, Canada, from 2009 to 2014 who had matching National Dental Examining Board of Canada (NDEB) data (N=298) were analyzed. In the results, significant differences between cohorts were found on both the NDEB objective structured clinical examination (OSCE) and written scores. Approximately 18% of the variation in OSCE scores was attributable to cohort differences and 82% to student differences. Approximately 10% of the variation in written scores was attributable to cohort differences and 90% to student differences. Several multilevel models were conducted. The final predictive model for NDEB OSCE scores consisted of age, Canadian Dental Aptitude Test (DAT) reading comprehension scores, year 2 average, and year 4 average. For predicting NDEB written exam scores, the final model consisted of DAT chemistry and year 1, 2, and 4 averages. The findings of this study showed that academic performance on admissions variables and in training predicted performance on dental licensing exams, whereas variables that captured noncognitive or interpersonal skills, such as interview scores, were not predictive. This difference may be due to construct mismatch, such that the outcome variables had no theoretical association with the predictors. Additional outcome measures (including noncognitive) are needed that have greater ecological validity in predicting potential for competence in 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".