Supporting self-regulated learning in medical school: a national survey
Bibliographic record
Abstract
Background: Learning plans (LPs) are an educational tool that allows learners to take an active role in their training and are therefore well suited to support self-regulated learning (SRL) within competency-based medical education (CBME). In medical education, LP use has been mostly explored at the postgraduate level, however undergraduate medical education (UGME) is also transitioning to CBME. An effective means of tracking students’ progress along their learning trajectory and preparing them for SRL in residency is not clear. Our recently conducted scoping review on LP use in UGME identified several benefits to LPs, suggesting they can be a useful tool to support SRL skill development for medical students. Objectives: Determine how Canadian medical schools are using LPs as well as identify barriers and facilitators to their use. Methods: We will follow Phillips et al’s (2022) six step approach for survey design. Results of a literature search on LP use in other health professions education programs combined with results of our scoping review will be used to generate a list of barriers and facilitators to LP use. Guided by goal-setting and SRL theories, a web-based survey questionnaire will be developed to assess LP use in 17 Canadian UGME programs. Data analysis will include descriptive, quantitative, and content analyses. Significance: Results will inform the creation of a framework for LP design, assessment, and implementation in Canadian UGME programs. Adoption of LPs at this stage of medical education may facilitate the translation of SRL skills into residency training.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".