Learning Plan Use in Undergraduate Medical Education: A Scoping Review
Bibliographic record
Abstract
PURPOSE: How to best support self-regulated learning (SRL) skills development and track trainees' progress along their competency-based medical education learning trajectory is unclear. Learning plans (LPs) may be the answer; however, information on their use in undergraduate medical education (UME) is limited. This study summarizes the literature regarding LP use in UME, explores the student's role in LP development and implementation, and identifies additional research areas. METHOD: MEDLINE, Embase, PsycInfo, Education Source, and Web of Science databases were searched for articles published from database inception to March 6, 2024, and relevant reference lists were manually searched. The review included studies of undergraduate medical students, studies of LP use, and studies of the UME stage in any geographic setting. Data were analyzed using quantitative and qualitative content analyses. RESULTS: The database search found 7,871 titles and abstracts with an additional 25 found from the manual search for a total of 7,896 articles, of which 39 met inclusion criteria. Many LPs lacked a guiding framework. LPs were associated with self-reported improved SRL skill development, learning structure, and learning outcomes. Barriers to their use for students and faculty were time to create and implement LPs, lack of training on LP development and implementation, and lack of engagement. Facilitators included SRL skill development, LP cocreation, and guidance by a trained mentor. Identified research gaps include objective outcome measures, longitudinal impact beyond UME, standardized framework for LP development and quality assessment, and training on SRL skills and LPs. CONCLUSIONS: This review demonstrates variability of LP use in UME. LPs appear to have potential to support medical student education and facilitate translation of SRL skills into residency training. Successful use requires training and an experienced mentor. However, more research is required to determine whether benefits of LPs outweigh the resources required for their use.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".