Influence of Training With Corrective Feedback Devices on Cardiopulmonary Resuscitation Skills Acquisition and Retention: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Several studies related to the use of corrective feedback devices in cardiopulmonary resuscitation training, with different populations, training methodologies, and equipment, present distinct results regarding the influence of this technology. OBJECTIVE: This systematic review and meta-analysis aimed to examine the impact of corrective feedback devices in cardiopulmonary resuscitation skills acquisition and retention for laypeople and health care professionals. Training duration was also studied. METHODS: The search was conducted in PubMed, Web of Science, and Scopus from January 2015 to December 2023. Eligible randomized controlled trials compared technology-based training incorporating corrective feedback with standard training. Outcomes of interest were the quality of chest compression-related components. The risk of bias was assessed using the Cochrane tool. A meta-analysis was used to explore the heterogeneity of the selected studies. RESULTS: In total, 20 studies were included. Overall, it was reported that corrective feedback devices used during training had a positive impact on both skills acquisition and retention. Medium to high heterogeneity was observed. CONCLUSIONS: This systematic review and meta-analysis suggest that corrective feedback devices enhance skills acquisition and retention over time. Considering the medium to high heterogeneity observed, these findings should be interpreted with caution. More standardized, high-quality studies are needed. TRIAL REGISTRATION: PROSPERO CRD42021240953; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=240953.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".