The effectiveness of academic admission interviews: an exploratory meta-analysis
Bibliographic record
Abstract
Admission to health-related professions is very competitive and selecting candidates with the best prospects for success is critical. A variety of measures are used to assess candidates to predict success. The purpose of this research was to assess the effectiveness of using selection interviews for admissions. Meta-analysis was applied to a sample of 20 studies examined in a comprehensive review article on the use of interviews in healthcare academic disciplines. Nineteen of these studies examined the relationship between performance in an interview situation and academic performance, while 10 examined the relationship between performance in an interview situation and clinical performance. A separate meta-analysis was conducted for each category of performance measure. The mean sample-size-effect size for studies examining the predictive power of interviews for academic success was 0.06 (95% confidence intervals 0.03-0.08), indicating a very small effect. The sample of studies was homogeneous using a fixed-effect model. The sample of studies for predicting clinical success had a mean effect size of 0.17 (95% confidence intervals 0.11-0.22), indicating modest positive predictive power. Using a random-effects model, this sample of studies was also homogeneous. Future research should investigate a larger sample of primary studies.
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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 | 0.069 | 0.178 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".