Pedagogical Implementation of Directive Feedback Manikins on Cardiopulmonary Resuscitation (CPR) Competencies: Expert Versus Peer Coaching
Bibliographic record
Abstract
BACKGROUND: Directive feedback manikins in resuscitation training evolved faster than the pedagogical evidence. Educators and learning systems must seek clarification on the efficacy of this technology to have evidence-based practices. This project explores directive feedback device use in cardiopulmonary resuscitation (CPR) education for laypersons. METHODS: A prospective nonrandomized-controlled design assessed two pedagogical approaches of directive feedback manikins in adult CPR lessons. The 230 participants were distributed between three groups: a control group without directive feedback manikins (no lights, NL), an expert coaching (EC) group with directive feedback and educator interpretation, and a peer coaching (PC) group with directive feedback, peer interpretation, and expert quality assurance. RESULTS: = .249). A chi-square test showed no significant association between groups and CPR skill feedback, or between groups and "recommending the course to a friend or family member." The PC group was more likely to agree that they could "coach someone to do CPR skills" than the NL or EC. CONCLUSIONS: This study expands the knowledge base of directive feedback manikins in a pedagogical setting to improve CPR competencies. Training organizations may consider any of these practices effective, choosing those that align with desired outcomes. CPR educators need orientation to feedback devices as well as professional development on educational options for their use. Considerations for further research include technology costs, access, and cultural aspects of implementing these tools.
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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 | 0.007 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".