Admission selection criteria as predictors of outcomes in an undergraduate medical course: A prospective study
Bibliographic record
Abstract
BACKGROUND: In 1998, a new selection process which utilised an aptitude test and an interview in addition to previous academic achievement was introduced into an Australian undergraduate medical course. AIMS: To test the outcomes of the selection criteria over an 11-year period. METHODS: 1174 students who entered the course from secondary school and who enrolled in the MBBS from 1999 through 2009 were studied in relation to specific course outcomes. Regression analyses using entry scores, sex and age as independent variables were tested for their relative value in predicting subsequent academic performance in the 6-year course. The main outcome measures were assessed by weighted average mark for each academic year level; together with results in specific units, defined as either 'knowledge'-based or 'clinically' based. RESULTS: Previous academic performance and female sex were the major independent positive predictors of performance in the course. The interview score showed positive predictive power during the latter years of the course and in a range of 'clinically' based units. This relationship was mediated predominantly by the score for communication skills. CONCLUSIONS: Results support combining prior academic achievement with the assessment of communication skills in a structured interview as selection criteria into this undergraduate medical course.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".