Readiness for Residency
Bibliographic record
Abstract
BACKGROUND: Health professions programs continue to search for meaningful and efficient ways to evaluate the quality of education they provide and support ongoing program improvement. Despite flaws inherent in self-assessment, recent research suggests that aggregated self-assessments reliably rank aspects of competence attained during preclerkship MD training. Given the novelty of those observations, the purpose of this study was to test their generalizability by evaluating an MD program as a whole. METHOD: The Readiness for Residency Survey (RfR) was developed and aligned with the published Readiness for Clerkship Survey (RfC), but focused on the competencies expected to be achieved at graduation. The RfC and RfR were administered electronically four months after the start of clerkship and six months after the start of residency, respectively. Generalizability and decision studies examined the extent to which specific competencies were achieved relative to one another. RESULTS: The reliability of scores assigned by a single resident was G = 0.32. However, a reliability of G = 0.80 could be obtained by averaging over as few as nine residents. Whereas highly rated competencies in the RfC resided within the CanMEDS domains of professional, communicator, and collaborator, five additional medical expert competencies emerged as strengths when the program was evaluated after completion by residents. CONCLUSIONS: Aggregated resident self-assessments obtained using the RfR reliably differentiate aspects of competence attained over four years of undergraduate training. The RfR and RfC together can be used as evaluation tools to identify areas of strength and weakness in an undergraduate medical education program.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".