Learning After the Simulation Is Over: The Role of Simulation in Supporting Ongoing Self-Regulated Learning in Practice
Bibliographic record
Abstract
The complex and dynamic nature of the clinical environment often requires health professionals to assess their own performance, manage their learning, and modify their practices based on self-monitored progress. Self-regulated learning studies suggest that while learners may be capable of such in situ learning, they often need guidance to enact it effectively. In this Perspective, the authors argue that simulation training may be an ideal venue to prepare learners for self-regulated learning in the clinical setting but may not currently be optimally fostering self-regulated learning practices. They point out that current simulation debriefing models emphasize the need to synthesize a set of identified goals for practice change (what behaviors might be modified) but do not address how learners might self-monitor the success of their implementation efforts and modify their learning plans based on this monitoring when back in the clinical setting. The authors describe the current models of simulation-based learning implied in the simulation literature and suggest potential targets in the simulation training process, which might be optimized to allow medical educators to take full advantage of the opportunity simulation provides to support and promote ongoing self-regulated learning in practice.
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.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".