Postapplication Advisement for U.S. Medical School Reapplicants: One School’s Program
Bibliographic record
Abstract
PROBLEM: Reapplicants make up over one-quarter of U.S. medical school applicants. Postapplication advisement (PAA) can provide potential reapplicants with concrete strategies for improvement, a contextualized basis for their scores, and a realistic idea of their chances for success. However, more data showing the effectiveness of PAA and an analysis of best practices are needed for PAA programs to be more widely adopted. APPROACH: In 2010, the University of New Mexico School of Medicine (UNM SOM) created a PAA program that involves a postapplication seminar (PAS), mandatory self-assessment and action plan development, and an individual consult with an admissions dean to prepare participants for reapplication. OUTCOMES: From 2010 to 2016, 892 applicants who interviewed and were rejected at UNM SOM were eligible to participate in PAA. Of these, 478 (53.6%) chose to participate in PAA over the seven-year period. Males had a higher participation rate (246/430; 57.2%) compared with females (232/461; 50.3%; P = .04). African Americans had a higher participation rate (12/17; 70.6%) and American Indian/Alaska Natives had a lower participation rate (17/64; 26.6%) than any other race/ethnicity. Of reapplicants who were subsequently accepted, 140/178 (78.7%) attended PAS and a consult, and 7/178 (3.9%) attended PAS only, compared with 31/178 (17.4%) of subsequently accepted reapplicants who did not participate in any PAA (P < .001). NEXT STEPS: Additional research should focus on the best approach for assisting reapplicants with prioritizing areas for improvement in their application. Demographic data may be used to target outreach to specific populations.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.013 |
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".