Patient Safety Education: Overreported and Still Lacking
Bibliographic record
Abstract
In Reply: We appreciate the comments by Dr. Kane and agree that the current state of patient safety education is not optimal in U.S. and Canadian medical schools. It is certainly possible that our data may have overestimated the prevalence of patient safety curricula within medical schools in those countries, as we queried a smaller sample of institutions than did Kane et al.1 Also, clerkship directors may have overestimated the number of curricula on this topic. Last, we did not survey to evaluate the total curricular time at each medical school devoted to patient safety education. We reported these limitations in our Academic Medicine article. Assessment of the CurrMIT system, as explained in Kane and colleagues' report, is another appropriate method for answering the same question. The 2008 article by Kane et al1 was not available at the time of our original literature search, and it should also be consulted by educators who wish to assess the literature in this area. Additional groups such as the Lucien Leape Institute in their recent report, Unmet Needs: Teaching Physicians to Provide Safe Patient Care,2 continue to call for more systematic, coordinated, longitudinal curricula on patient safety in undergraduate education. Regardless of the precise extent of patient safety curricula within our medical schools, it is clear that we are not doing enough. We believe that coordinated efforts should continue to occur and that periodic monitoring of the extent of adoption of patient safety curricula should also continue, through additional research studies and, more formally, through the mechanisms of the LCME and the AAMC. Eric Alper, MD Associate professor of medicine, University of Massachusetts Medical School, Worcester, Massachusetts; [email protected]. Steven Durning, MD Professor of medicine and pathology, Uniformed Services University of the Health Sciences, Bethesda, Maryland.
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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 | 0.011 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.030 | 0.039 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".