Personality as a Predictor of Professional Behavior in Dental School: Comparisons with Dental Practitioners
Bibliographic record
Abstract
The purpose of this study was to examine the use of personality measures to predict the success of dental students (N = 87) in clinical and academic courses and to compare their personality profiles to those of dental practitioners (N = 130). A second purpose of the study was to develop a new criterion measure, the Student Professionalism Scale, based on competencies previously identified as necessary for professional success. The Canadian Dental Aptitude Test (DAT) predicted first-year, preclinical academic success; the DAT Reading Comprehension component predicted third-year clinical performance; and Perceptual Ability, the ability to deal with two- and three-dimensional objects, predicted student professionalism. Results from the personality measure indicated that Conscientiousness and Neuroticism, and to a lesser extent Agreeableness, were significant predictors of both first-year academic performance and professional behavior. In comparing the personality profiles of dental students to dental practitioners, students who were more similar to the dentists did better in their first year of coursework. Implications of the findings are discussed in the context of the dental admissions process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".