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Record W4393361488 · doi:10.2196/52230

Effectiveness of Blended Versus Traditional Refresher Training for Cardiopulmonary Resuscitation: Prospective Observational Study

2024· article· en· W4393361488 on OpenAlexvenueno aff
Cheng‐Yu Chien, Shang-Li Tsai, Chien‐Hsiung Huang, Ming‐Fang Wang, Chi‐Chun Lin, Chen‐Bin Chen, Li‐Heng Tsai, Hsiao‐Jung Tseng, Yan-Bo Huang, Chip‐Jin Ng

Bibliographic record

VenueJMIR Medical Education · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsCardiopulmonary resuscitationPreprintMedicineTraining (meteorology)Medical educationResuscitationEmergency medicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Generally, cardiopulmonary resuscitation (CPR) skills decline substantially over time. By combining web-based self-regulated learning with hands-on practice, blended training can be a time- and resource-efficient approach enabling individuals to acquire or refresh CPR skills at their convenience. However, few studies have evaluated the effectiveness of blended CPR refresher training compared with that of the traditional method. OBJECTIVE: This study investigated and compared the effectiveness of traditional and blended CPR training through 6-month and 12-month refresher sessions with CPR ability indicators. METHODS: This study recruited participants aged ≥18 years from the Automated External Defibrillator Donation Project. The participants were divided into 4 groups based on the format of the CPR training and refresher training received: (1) initial traditional training (a 30-minute instructor-led, hands-on session) and 6-month traditional refresher training (Traditional6 group), (2) initial traditional training and 6-month blended refresher training (an 18-minute e-learning module; Mixed6 group), (3) initial traditional training and 12-month blended refresher training (Mixed12 group), and (4) initial blended training and 6-month blended refresher training (Blended6 group). CPR knowledge and performance were evaluated immediately after initial training. For each group, following initial training but before refresher training, a learning effectiveness assessment was conducted at 12 and 24 months. CPR knowledge was assessed using a written test with 15 multiple-choice questions, and CPR performance was assessed through an examiner-rated skill test and objectively through manikin feedback. A generalized estimating equation model was used to analyze changes in CPR ability indicators. RESULTS: This study recruited 1163 participants (mean age 41.82, SD 11.6 years; n=725, 62.3% female), with 332 (28.5%), 270 (23.2%), 258 (22.2%), and 303 (26.1%) participants in the Mixed6, Traditional6, Mixed12, and Blended6 groups, respectively. No significant between-group difference was observed in knowledge acquisition after initial training (P=.23). All groups met the criteria for high-quality CPR skills (ie, average compression depth: 5-6 cm; average compression rate: 100-120 beats/min; chest recoil rate: >80%); however, a higher proportion (98/303, 32.3%) of participants receiving blended training initially demonstrated high-quality CPR skills. At 12 and 24 months, CPR skills had declined in all the groups, but the decline was significantly higher in the Mixed12 group, whereas the differences were not significant between the other groups. This finding indicates that frequent retraining can maintain high-quality CPR skills and that blended refresher training is as effective as traditional refresher training. CONCLUSIONS: Our findings indicate that 6-month refresher training sessions for CPR are more effective for maintaining high-quality CPR skills, and that as refreshers, self-learning e-modules are as effective as instructor-led sessions. Although the blended learning approach is cost and resource effective, factors such as participant demographics, training environment, and level of engagement must be considered to maximize the potential of this approach. TRIAL REGISTRATION: IGOGO NCT05659108; https://www.cgmh-igogo.tw.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.394
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2024
Admission routes1
Has abstractyes

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