Personality Perceptions of Medical School Applicants
Bibliographic record
Abstract
Purpose To examine the extent to which medical school interviewers consider perceptions of applicant personality traits during a semi-structured panel interview, the interrater reliability of assessments, and the impact of such perceptions on individual admission decisions. Method Semi-structured panel interviews were conducted with applicants to the Doctor of Medicine Program at the University of Western Ontario in London, Canada. Interviewers also provided voluntary, "research only" ratings of applicants on nine relevant personality traits. Data from 345 applicants under consideration for admission were available for analysis. Results Significant correlations were observed between personality ratings and important operational variables (e.g., interview scores). Applicants who were most likely to be admitted to the program were perceived as high on certain traits (i.e., Achievement, Nurturance, Endurance, Cognitive Structure, & Order) and low on other traits (i.e., Abasement, Aggression, & Impulsivity). Statistically removing variance shared with personality ratings from interview scores resulted in different admission decisions for over 40% of the applicants. Interrater reliabilities for personality perceptions were relatively low. However, interrater reliability of the panel interview used to make admission decisions was acceptable. Nonlinear relations between personality perceptions and interview scores were also explored. Conclusion Some evidence was found that interviewers? perceptions of applicant personality may affect their judgments when assigning interview ratings. Given that non-cognitive characteristics are perceived as important in the admissions process and that perceptions of personality traits have implications for decisions about which candidates to admit, suggestions for identifying desirable non-cognitive characteristics and for increasing the quality of assessments are offered.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.609 | 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 teacher head, 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".