Curricula for teaching the content of clinical practice guidelines to family medicine and internal medicine residents in the US: a survey study
Bibliographic record
Abstract
BACKGROUND: Teaching the content of clinical practice guidelines (CPGs) is important to both clinical care and graduate medical education. The objective of this study was to determine the characteristics of curricula for teaching the content of CPGs in family medicine and internal medicine residency programs in the United States. METHODS: We surveyed the directors of family medicine and internal medicine residency programs in the United States. The questionnaire included questions about the characteristics of the teaching of CPGs: goals and objectives, educational activities, evaluation, aspects of CPGs that the program teaches, the methods of making texts of CPGs available to residents, and the major barriers to teaching CPGs. RESULTS: Of 434 programs responding (out of 839, 52%), 14% percent reported having written goals and objectives related to teaching CPGs. The most frequently taught aspect was the content of specific CPGs (76%). The top two educational strategies used were didactic sessions (76%) and journal clubs (64%). Auditing for adherence by residents was the primary evaluation strategy (44%), although 36% of program directors conducted no evaluation. Programs made texts of CPGs available to residents most commonly in the form of paper copies (54%) while the most important barrier was time constraints on faculty (56%). CONCLUSION: Residency programs teach different aspects of CPGs to varying degrees, and the majority uses educational strategies not supported by research evidence.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".