Creating a reliable and valid blueprint for the internal medicine clerkship evaluation
Bibliographic record
Abstract
The objective of this study was to design an examination blueprint for the Internal Medicine clerkship rotation that is congruent with both the learning objectives and delivered learning experiences and reflects the perceived importance of clinical presentations from both the students' and clinicians' perspectives. In this cross-sectional study 11 specialists in General Internal Medicine (GIM) and 11 clinical clerks at the University of Calgary were asked to score each of the 47 clinical presentations in the Internal Medicine clerkship rotation for 'impact' and 'frequency'. These attributes were used to provide an estimate of the relative importance of each clinical presentation. Statistical tests used were the Pearson's correlation coefficient and the Kappa statistic. Multi-attribute utility theory was applied to assess the best way of combining the variables of 'impact' and 'frequency'. The correlation between clerks and GIM specialists was 0.85 for the impact score and 0.86 for the frequency score (p < 0.001 for both). Corresponding Kappa values were 0.71 and 0.82, respectively (p < 0.001 for both). Combining impact and frequency as a multiplicative function produced a distribution that was positively skewed towards common, high impact presentations such as chest pain. We have created an examination blueprint that provides a realistic and objective measure of the relative importance of clinical presentations. Such a blueprint provides both face validity and content validity to the evaluation process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".