High dives and parallel plans: relationships between medical student elective strategies and residency match outcomes
Bibliographic record
Abstract
BACKGROUND: Medical students are anxious about not getting a preferred residency position. We described elective patterns of two recent cohorts and examined associated match outcomes. METHODS: We conducted a retrospective review of the final-year electives of all students who participated in the residency match (first iteration) at one school for 2017 and 2018. We categorized elective patterns and associated them with aggregated match outcomes. We examined high-demand/low-supply (HDLS) disciplines separately. RESULTS: We described three elective patterns: High Dive, Parallel Plan(s), and No Clear Pattern. Many students had High Dive and Parallel Plans patterns; only a few showed No Clear Pattern. Match rates for High Dive and Parallel Plan patterns were high but many students matched to Family and Internal Medicine. When we separated out HDLS predominance, the match rate remained high but a significant number matched to disciplines in which they did not have a majority of electives. Most High Dive and Parallel Plan students who went unmatched did so with HDLS discipline electives. CONCLUSION: Many students chose High Dive and Parallel Plan strategies to both high-capacity and HDLS disciplines. Match rates were high for both patterns but students also matched to non-primary disciplines. Back-up planning may reside in the entire application, and not just electives selection.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".