The nature of learning from simulation: Now I know it, now I'll do it, I'll work on that
Bibliographic record
Abstract
CONTEXT: Ongoing learning in complex clinical environments requires health professionals to assess their own performance, manage their learning, and modify their practices based on self-monitored progress. Self-regulated learning (SRL) theory suggests that although learners may be capable of such learning, they often need guidance to enact it effectively. Debriefings following simulation may be an ideal time to support learners' use of SRL in targeted areas, but the extent to which they are optimally fostering these practices has not been examined. METHODS: A qualitative study informed by grounded theory methodology was conducted in the context of three interprofessional in situ trauma simulations at our level 1 trauma centre. A total of 18 participants were interviewed both immediately and 5-6 weeks after the simulation experience. Transcripts were analysed using an iterative constant comparative approach to explore concepts and themes regarding the nature of learning from and after simulation. RESULTS: During initial interviews, there were many examples of acquired content knowledge and straightforward practice changes that might not require ongoing SRL to enact well in practice. However, even for skills identified as needing to be 'worked on,' SRL strategies were lacking. At follow-up interviews, some participants had evolved more specific learning goals and rudimentary plans for implementation and improvement, but suggested this was prompted by the study interview questions rather than the simulation debriefing itself. CONCLUSIONS: Overall, participants did not engage in fulsome development of SRL plans based on the simulation and debriefing; however, there were elements of SRL present, particularly after participants were given time to reflect on the interview questions and their own goals. This suggests that simulation training can support the use of SRL. However, debriefing approaches might be better optimised to take full advantage of the opportunity to encourage and foster SRL in practice after the simulation is over.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".