Learning communities in medical education: A scoping review protocol
Bibliographic record
Abstract
Medical schools are enriching programs that form the foundation for students’ lifelong careers as physicians. However, along with all of the positive outcomes and experiences part of medical school, many students report feelings of burnout, stress and/or depression. For example, a 2006 systematic review found that United States and Canadian medical students’ levels of psychological distress were consistently higher than the age-matched general population, and that this trend continued throughout each year of training1. Online and hybrid learning have also further contributed to these sentiments along with isolation and a lack of connectedness. A study conducted in nine medical schools in the state of Florida used questionnaires to evaluate the top stressors of medical students and their effects 2. These included medical school peer relations and conflicts in work-life balance and relationships, which caused effects ranging from poor academic performance, decreases in empathy, suicidal ideation, or leaving medical school. Therefore, further studying and finding creative ways to improve the overall well-being and academic success of medical students is of keen interest. A method in which this has been done is through establishing learning communities in medical schools. In broad terms, learning communities involve small group activities between students and selected faculty mentors, with an emphasis on community, collaboration, and professional development. Learning communities vary in their implementation, size, importance, and individual components throughout different medical schools. Components of learning communities may include but are not limited to social activities, small group reflections, having a dedicated physical location on campus for each small group, or integrating learning communities and clinical skills teaching together. There have been studies conducted centered around different learning communities currently present throughout medical schools, as well as their composition and comprehensive effects on students. The goal of this scoping review is to analyze overarching trends from the literature to piece vital aspects together. Emphasis will be placed on finding core aspects and protocols of learning communities that proved to be most important to medical students and or faculty and their experience. Accomplishing this will hopefully enable medical schools to create learning communities that effectively enhance medical education and lifelong skills, values, and attitudes needed for strong careers as physicians.
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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 | 0.076 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.012 |
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".