An Equivalence Trial Comparing Instructor-Regulated With Directed Self-Regulated Mastery Learning of Advanced Cardiac Life Support Skills
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Instructor-led simulation-based mastery learning of advanced cardiac life support (ACLS) skills is an effective and focused approach to competency-based education. Directed self-regulated learning (DSRL) may be an effective and less resource-intensive way to teach ACLS skills. METHODS: Forty first-year internal medicine residents were randomized to either simulation-based DSRL or simulation-based instructor-regulated learning (IRL) of ACLS skills using a mastery learning model. Residents in each intervention completed pretest, posttest, and retention test of their performance in leading an ACLS response to a simulated scenario. Performance tests were assessed using a standardized checklist. Residents in the DSRL intervention were provided assessment instruments, a debriefing guide, and scenario-specific teaching points, and they were permitted to access relevant online resources. Residents in the IRL intervention had access to the same materials; however, the teaching and debriefing were instructor led. RESULTS: Skills of both the IRL and DSRL interventions showed significant improvement after the intervention, with an average improvement on the posttest of 21.7%. After controlling for pretest score, there was no difference between intervention arms on the posttest [F(1,37) = 0.02, P = 0.94] and retention tests [F(1,17) = 1.43, P = 0.25]. Cost savings were realized in the DSRL intervention after the fourth group (16 residents) had completed each intervention, with an ongoing savings of $80 per resident. CONCLUSIONS: Using a simulation-based mastery learning model, we observed equivalence in learning of ACLS skills for the DSRL and IRL conditions, whereas DSRL was more cost effective.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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