Using the Readiness for Clerkship and Residency Surveys to Evaluate the Effectiveness of Four MD Programs: A Cross-Institutional Generalizability Study
Bibliographic record
Abstract
PURPOSE: The importance of confidence for learning and performance makes learners' perceptions of readiness for the next level of training valuable indicators of curricular success. The "Readiness for Clerkship" (RfC) and "Readiness for Residency" (RfR) surveys have been shown to provide reliable ratings of the relative effectiveness of various aspects of training. This study examines the generalizability of those results. METHOD: Surveys were administered at four medical schools approximately four months after the start of clerkship and eight months after the start of residency during 2013-2015. Collected data were anonymized. A total of 647 medical students and 483 residents participated. RESULTS: Reliabilities of G = 0.8 could be obtained with only 6 to 12 medical students and 8 to 15 residents. Within MD programs, no meaningful differences in item ratings were observed across cohorts. Residents in each school consistently rated themselves higher than clerks on the majority of Medical Expert and Communicator competencies common to both surveys. Similar strengths and weaknesses were identified across programs, but differences were observed on five clerkship items and one residency item. CONCLUSIONS: Across four MD programs, the RfC and RfR surveys provided reliable ratings of the relative effectiveness of aspects of training with small numbers of respondents. The capacity of these surveys to efficiently identify perceived strengths and weaknesses held by cohorts of learners may, thereby, facilitate program improvement.
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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 | 0.035 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".