A reflective analysis of medical education research on self‐regulation in learning and practice
Bibliographic record
Abstract
OBJECTIVES: In the health professions we expect practitioners and trainees to engage in self-regulation of their learning and practice. For example, doctors are responsible for diagnosing their own learning needs and pursuing professional development opportunities; medical residents are expected to identify what they do not know when caring for patients and to seek help from supervisors when they need it, and medical school curricula are increasingly called upon to support self-regulation as a central learning outcome. Given the importance of self-regulation in both health professions education and ongoing professional practice, our aim was to generate a snapshot of the state of the science in medical education research in this area. METHODS: To achieve this goal, we gathered literature focused on self-regulation or self-directed learning undertaken from multiple perspectives. Then, with support from a multi-component theoretical framework, we created an overarching map of the themes addressed thus far and emerging findings. We built from that integrative overview to consider contributions, connections and gaps in research on self-regulation to date. RESULTS AND CONCLUSIONS: Based on this reflective analysis, we conclude that the medical education community's understanding about self-regulation will continue to advance as we: (i) consider how learning is undertaken within the complex social contexts of clinical training and practice; (ii) think of self-regulation within an integrative perspective that allows us to combine disparate strands of research and to consider self-regulation across the training continuum in medicine, from learning to practice; (iii) attend to the grain size of analysis both thoughtfully and intentionally, and (iv) most essentially, extend our efforts to understand the need for and best practices in support of self-regulation.
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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 | 0.110 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| 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".