On the challenges of embedding assessments of self-regulated learning into licensure activities in health professions education
Bibliographic record
Abstract
How can we claim that we are creating "lifelong learners" if we are not embedding assessments of self-regulated learning (SRL) into health professions education (HPE)? A good question but one that we must not try to answer too hastily. Some may consider SRL to be such an important competency that failing to assess it disservices everyone involved in HPE, including patients. I would argue that assessment of SRL may well be justified, but that how it is measured, what we might find, and what the implications of those findings might be, are equally critical to consider. The fact is that learners in HPE face many pressures that influence not just the quantity but also the quality of their self-regulation towards learning, which measures of SRL would have to account for, to be effective. Drawing on the self-regulation literature and self-determination theory (SDT) in particular, my aim in the present commentary is to discuss some of the nuances and issues that we would need to address, if we were to move towards a unified approach to assessing SRL in HPE.
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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 | 0.197 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.026 | 0.050 |
| 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".