Unveiling CPR training challenges in nursing education: Pedagogical strategies for success
Bibliographic record
Abstract
AIM: This study explored the challenges nursing students face while learning CPR and identified experiential learning strategies to address these challenges. BACKGROUND: Nursing students often experience challenges and anxiety during clinical learning, including CPR training. Given the experimental nature of CPR training, experiential learning models like mARC can significantly enhance the learning experience by addressing these prevalent challenges. DESIGN: This study adopts an interpretivist approach within a qualitative methodology and uses a phenomenological design. METHOD: Semi-structured interviews and the Delphi method were used to gather firsthand experiences from 37 educational supervisors, nursing professors and nursing students undergoing CPR clinical training at five public medical universities. RESULTS: Four main challenges and eighteen sub-challenges of CPR training were identified, elaborated and modeled. Additionally, thirteen experiential learning strategies, based on the mARC experiential learning model (more Authentic, Reflective, Collaborative), were mapped to address these challenges. CONCLUSIONS: Among the four main challenges of CPR training identified by this study, the lack of pedagogy appears to be the underlying cause of the other three. This underscores the significance of integrating effective pedagogical approaches into nurse education strategies and initiatives.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".