An Equivalence Trial Comparing Instructor-Regulated With Directed Self-Regulated Mastery Learning of Advanced Cardiac Life Support Skills
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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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".