Defining and Describing Medical Learning Communities: Results of a National Survey
Bibliographic record
Abstract
PURPOSE: To investigate what is meant by learning community in medical education and to identify the most important features of current medical education learning communities. METHOD: After a literature review, the authors surveyed academic deans of all U.S. and Canadian medical schools and colleges (N=124) to identify those that had implemented a learning community. Those with student learning communities (N=18) answered a series of questions about the goals, structure, function, benefits, and challenges of their communities. RESULTS: The most common primary goals included fostering communication among students and faculty; promoting caring, trust, and teamwork; helping students establish academic support networks; and helping students establish social support networks. Most deans said that students remained in the same community for all four years of medical school and that communities were linked to specific faculty and/or peer advisors. For most schools, communities included students from many class years, and participation was mandatory. Curricular purposes included professionalism training, leadership development, and service learning. Almost all schools had social functions related to their communities, and most provided career planning, group mentoring, and personal counseling. CONCLUSIONS: Learning communities in medical education demonstrate diverse approaches to achieving the general goal of enhanced student learning. Medical school leaders considering learning communities should determine the goals they want to accomplish and be open to adopting different approaches based on local needs. Evaluation and effective monitoring of evolution are needed to determine the best approaches for different needs and to assess impact on students and faculty.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".