PRESCRIBING COMPETENCY OF MEDICAL STUDENTS: NATIONAL SURVEY OF MEDICAL EDUCATION LEADERS
Bibliographic record
Abstract
Introduction Evidence suggests that newly licensed physicians are not adequately prepared to prescribe safely. There is currently no national pre-licensure prescribing competency assessment required in North America. This study's purpose was to survey Canadian medical school leaders for their interest in and perceived need for a nationwide prescribing assessment for final year medical students. Method In spring of 2015, surveys were disseminated online to medical education leaders in all 17 Canadian medical schools. The survey included questions on perceived prescribing competency in medical schools, and interest in integration of a national assessment into medical school curricula and licensing. Results 372 (34.6 %) faculty from all 17 Canadian medical schools responded. 277 (74.5%) respondents were residency directors, 33 (8.9%) vice deans of medical education or equivalent, and 62 (16.7%) clerkship coordinators. Faculty judged 23.4% (SD 22.9%) of their own graduates' prescribing knowledge to be unsatisfactory and 131 (44.8%) felt obligated to provide close supervision to more than a third of their new residents due to prescribing concerns. 239 (73.0%) believed that an assessment process would improve their graduates' quality, 262 (80.4%) thought it should be incorporated into their medical school curricula and 248 (76.0%) into the national licensing process. Except in regards to close supervision due to concerns, there were no significant differences between schools' responses. Conclusions Amongst Canadian medical school leadership, there is a perceived inadequacy in medical student prescribing competency as well as support for a standardized prescribing competency assessment in curricula and licensing processes.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".