Teaching evidence-based medical care: description and evaluation.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This paper describes and evaluates several years of a seminar series designed to stimulate residents to seek evidence-based answers to their clinical questions and incorporate this evidence into practice. METHODS: At the first session, 86 of 89 (97%) residents completed a baseline needs assessment questionnaire. Post-course self-assessment questionnaires measured change from the first to the final seminar session in six domains of interest and skill, as well as residents' preferred sources of information for clinical problem solving up to 2 years after the course. RESULTS: Before the seminars, 48% of residents reported that textbooks were their most important source of information for solving clinical problems. A total of 58 of 75 (77%) residents completed the first post-course questionnaire. Residents reported significant increases in skill at formulating clinical questions and searching for evidence-based answers, appraising reviews, and deciding when and how to incorporate new findings into practice. Use of secondary sources of information such as "Best Evidence," moved up in importance from before the course to after the course. CONCLUSIONS: First-year family practice residents who completed our seminar series have reported increased skill at blending consideration of a clinical problem with the use of secondary sources of information to access evidence to support their health care decisions.
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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 | 0.033 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".