Resident Selection for a Physical Medicine and Rehabilitation Program
Bibliographic record
Abstract
The development of a process to select the best residents for training programs is challenging. There is a paucity of literature to support the implementation of an evidence-based approach or even best practice for program directors and selection committees. Although assessment of traditional academic markers such as clerkship grades and licensing examination scores can be helpful, these measures typically fail to capture performance in the noncognitive domains of medicine. In the specialty of physical medicine and rehabilitation, physician competencies such as communication, health advocacy, and managerial and collaborative skills are of particular importance, but these are often difficult to evaluate in admission interviews. Recent research on admission processes for medical schools has demonstrated reliability and validity of the "multiple mini-interview." The objective of our project was to develop and evaluate the multiple mini-interview for a physical medicine and rehabilitation residency training program, with a focus on assessment of the noncognitive physician competencies. We found that the process was feasible, time efficient, and cost-efficient and that there was good interrater reliability. The multiple mini-interview may be applied to other physical medicine and rehabilitation residency programs. Further research is needed to confirm reliability and determine validity.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".