Pilot study of the roles of personality, references, and personal statements in relation to performance over the five years of a medical degreeCommentary: How to derive causes from correlations in educational studies
Bibliographic record
Abstract
OBJECTIVES: To compare the power of three traditional selection procedures (A levels, personal statements, and references) and one non-traditional selection procedure (personality) to predict performance over the five years of a medical degree. DESIGN: Cohort study over five years. SETTING: Nottingham medical school. PARTICIPANTS: Entrants in 1995. MAIN OUTCOME MEASURES: A level grades, amounts of information contained in teacher's reference and the student's personal statement, and personality scores examined in relation to 18 different assessments. RESULTS: Information in the teacher's reference did not consistently predict performance. Information in the personal statement was predictive of clinical aspects of training, whereas A level grades primarily predicted preclinical performance. The personality domain of conscientiousness was consistently the best predictor across the course. A structural model indicated that conscientiousness was positively related to A level grades and preclinical performance but was negatively related to clinical grades. CONCLUSION: A teacher's reference is of no practical use in predicting clinical performance of medical students, in contrast to the amount of information contained in the personal statement. Therefore, simple quantification of the personal statement should aid selection. Personality factors, in particular conscientiousness, need to be considered and integrated into selection procedures.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".