Standardized Patient Practices: Initial Report on the Survey of US and Canadian Medical Schools
Bibliographic record
Abstract
Frequency: Yearly eISSN: 1087-2981 https://www.tandfonline.com/doi/abs/10.1080/10872981.2021.1984177 ABSTRACT Background Firearm violence is a unique public health crisis in the USA (US). A majority of U.S. physicians believe they should discuss firearm safety with patients. However, little education on firearm injury prevention and counseling exists in medical school. We sought to address this gap by creating a curriculum on firearm violence as a part of a required preclinical medical school course focused on health policy issues. Methods The Kerns 6-step model for curriculum development was used to define the problem and assess learner needs. The two-hour small group session was co-authored by a student and faculty member to address the course theme of health policy as applied to firearm violence. The Issue-Attention Cycle, history of firearm policy, and US politics were incorporated from published literature, with a patient counseling role-play added in 2019. Results The ‘Current Case in Health Policy – Firearm Violence’ small group was implemented in 2018 and 2019 for all first-year medical students. Of the 2018 student evaluations, 57% selected this small group as the most valuable in the course. In a follow-up survey in 2020, 78% of the respondents agreed that they felt more confident counseling patients on firearm safety following the role-play. Conclusion Students broadly endorsed the incorporation of firearm policy and counseling skills into medical education. This curriculum can be adapted for learners at all stages of training, especially given the limited exposure to this topic in medical education.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".