Personality and a new Canadian Dental Association interview : implications for dental student selection
Bibliographic record
Abstract
L'auteur a accordé une licence non exclusive permettant à la Bibliothèque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par télécommunication ou par l'Internet, prêter, distribuer et vendre des thèses partout dans le monde, à des fins commerciales ou autres, sur support microforme, papier, électronique et/ou autres formats.Dental Student Selection 5 List of Tables Page 1. Descriptions o f the narrow facets o f the Big Five Personality D im ensions 18 2. Stratification o f Student Sample across Gender and Year o f Study 30 3. Stratification o f Dentist Sample across Gender and Current Employment 4. Correlations among Control, Predictor, and Criterion Variables 39 5. Correlations for Narrow Facets o f the B ig Five and Dental School Performance 6. Hierarchical Regression Year 1 Performance on Personality 7. Hierarchical Regression Year 1 Performance on Interview Scores and Personality 8. Hierarchical Regression o f Year 2 Clinical Courses on Personality 9. Hierarchical Regression o f Year 2 Clinical Courses on Interview Scores and Personality 10.Hierarchical Regression o f Year 2 Academic Courses on Personality 11.Hierarchical Regression o f Year 2 Academic Courses on Interview Scores and Personality 12. Hierarchical Regression o f Year 3 Clinical Courses on Personality 13.Hierarchical Regression o f Year 3 Academic Courses on Personality 14. Hierarchical Regression o f BARS Ratings on Personality 15.Hierarchical Regression o f BARS Ratings on Interview Scores and Personality 16.Regression o f B ig Five factors on Year o f Study 17. Profile Matching using Dental Students and Dentists 18. Interview Questions and the B ig F ive Personality Factors Dental Student Selection 6
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".