A Scientific Approach to Preparation for Residency Interviews
Bibliographic record
Abstract
Residency programs in the United States and Canada are faced with the difficult task of assessing and ranking applicants for the National Resident Matching Program. Grades, United States Medical Licensure Examination (USMLE) scores, recommendations, and internet-based sources of information impact the decision to offer an interview. Once an on-site interview has been granted, this contact becomes central to the residency program’s goal of populating their residency with individuals who have the best chance of surviving and thriving and the applicant’s goal of gaining admission. Standardized, structured interviews, such as the behavioral based interview (BBI) ensure consistency in the style of questions and method of grading applicants. Preparation for this style of interview will improve the odds of gaining acceptance to a program. Applicants should use the same technique to evaluate the residency program and determine if it best fits their needs and aspirations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.227 | 0.275 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".