The Readiness for Clerkship Survey
Bibliographic record
Abstract
PURPOSE: To examine whether or not aggregated self-assessment data of clerkship readiness can provide meaningful sources of information to evaluate the effectiveness of an educational program. METHOD: The 39-item Readiness for Clerkship survey was developed during academic year 2009-2010 using several key competence documents and expert review. The survey was completed by two cohorts of students (179 from the class of 2011 in February 2010, 171 from the class of 2012 in November 2010) and of clinical preceptors (384 for class of 2011 preceptors, 419 for class of 2012 preceptors). Descriptive statistics, Pearson correlations coefficients, ANOVA, and generalizability and decision studies were used to determine whether ratings could differentiate between different aspects of a training program. RESULTS: When self-assessments were aggregated across students, their judgments aligned very well with those of faculty raters. The correlation of average scores, calculated for each item between faculty and students, was r=0.88 for 2011 and r=0.91 for 2012. This was only slightly lower than the near-perfect correlations of item averages within groups across successive years (r=0.99 for faculty; r=0.98 for students). Generalizability and decision analyses revealed that one can achieve interrater reliability in this domain with fewer students (9-21) than faculty (26-45). CONCLUSIONS: These results provide evidence that, when aggregated, student self-assessment data from the Readiness for Clerkship Survey provide valid data for use in program evaluation that align well with an external standard.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".