A reflective analysis of medical education research on self‐regulation in learning and practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it