Assessing medical student knowledge and attitudes about shared decision making across the curriculum: protocol for an international online survey and stakeholder analysis
Bibliographic record
Abstract
INTRODUCTION: Shared decision making (SDM) is a goal of modern medicine; however, it is not currently embedded in routine care. Barriers include clinicians’ attitudes, lack of knowledge and training and time constraints. Our goal is to support the development and delivery of a robust SDM curriculum in medical education. Our objective is to assess undergraduate medical students’ knowledge of and attitudes towards SDM in four countries. METHODS AND ANALYSIS: The first phase of the study involves a web-based cross-sectional survey of undergraduate medical students from all years in selected schools across the United States (US), Canada and undergraduate and graduate students in the Netherlands. In the United Kingdom (UK), the survey will be circulated to all medical schools through the UK Medical School Council. We will sample students equally in all years of training and assess attitudes towards SDM, knowledge of SDM and participation in related training. Medical students of ages 18 years and older in the four countries will be eligible. The second phase of the study will involve semistructured interviews with a subset of students from phase 1 and a convenience sample of medical school curriculum experts or stakeholders. Data will be analysed using multivariable analysis in phase 1 and thematic content analysis in phase 2. Method, data source and investigator triangulation will be performed. Online survey data will be reported according to the Checklist for Reporting the Results of Internet E-Surveys. We will use the COnsolidated criteria for REporting Qualitative research for all qualitative data. ETHICS AND DISSEMINATION: The study has been approved for dissemination in the US, the Netherlands, Canada and the UK. The study is voluntary with an informed consent process. The results will be published in a peer-reviewed journal and will help inform the inclusion of SDM-specific curriculum in medical education worldwide.
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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 | 0.098 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.015 |
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".