Publication:
Deconstructing resuscitation training for healthcare providers: a protocol for a component network meta-analysis.

cris.virtual.author-orcid0000-0002-0955-7572
cris.virtualsource.author-orcide1dba832-8d83-4311-9d71-ba02eaa0afba
datacite.rightsopen.access
dc.contributor.authorEfendi, Defi
dc.contributor.authorCheng, Adam
dc.contributor.authorWanda, Dessie
dc.contributor.authorFurukawa, Toshi A
dc.contributor.authorPetropoulou, Maria
dc.contributor.authorEfthimiou, Orestis
dc.contributor.authorChen, Kee-Hsin
dc.date.accessioned2025-07-30T06:54:48Z
dc.date.available2025-07-30T06:54:48Z
dc.date.issued2025-07-25
dc.description.abstractIntroduction The necessity of enhancing resuscitation training has been encouraged by The International Liaison Committee on Resuscitation and the American Heart Association to reduce mortality, disability and healthcare costs. Resuscitation training is a complicated approach that encompasses various components and their mixture. It is essential to identify the most effective of these components and their combinations, to measure the corresponding effect size and to understand which participant groups may enjoy the greatest advantage.Methods And Analysis We will systematically search 12 databases and two clinical trial registries for randomised controlled trials (RCTs) that examine different resuscitation training methods from inception to April 2025. The analysis will be carried out using the standard network meta-analysis and component network meta-analysis models. Resuscitation skills of staff will be the primary outcome of this analysis. Paired reviewers will independently screen and extract data. A consensus will be sought with the principal investigators to resolve any disagreements that cannot be achieved through regular meetings. Each intervention in each RCT will be decomposed according to its constituent components, such as delivery method, interactivity, teamwork, digitalisation and type of simulator. The analysis will be conducted using the frequentist and bayesian approach in the R environment. RoB V.2.0 and Confidence in Network Meta-Analysis will, respectively, be used to assess the risk of bias and the certainty of the evidence.Ethics And Dissemination As we will use only aggregated secondary data without individual identities, ethical approval is not required. Results of this review will be shared through a peer-reviewed publication and presentation of papers at any relevant conferences.Prospero Registration Number CRD42024532878.
dc.description.numberOfPages10
dc.description.sponsorshipInstitute of General Practice and Primary Care (BIHAM)
dc.identifier.doi10.48620/90447
dc.identifier.pmid40713054
dc.identifier.publisherDOI10.1136/bmjopen-2024-094869
dc.identifier.urihttps://boris-portal.unibe.ch/handle/20.500.12422/214009
dc.language.isoen
dc.publisherBMJ Publishing Group
dc.relation.ispartofBMJ Open
dc.relation.issn2044-6055
dc.subjectCardiopulmonary Resuscitation
dc.subjectEDUCATION & TRAINING (see Medical Education & Training)
dc.subjectHealth Care Costs
dc.subjectNetwork Meta-Analysis
dc.subjectSystematic Review
dc.subject.ddc600 - Technology::610 - Medicine & health
dc.titleDeconstructing resuscitation training for healthcare providers: a protocol for a component network meta-analysis.
dc.typearticle
dspace.entity.typePublication
dspace.file.typetext
oaire.citation.issue7
oaire.citation.startPagee094869
oaire.citation.volume15
oairecerif.author.affiliationInstitute of General Practice and Primary Care (BIHAM)
unibe.contributor.orcid0000-0002-0955-7572
unibe.contributor.roleauthor
unibe.description.ispublishedpub
unibe.journal.abbrevTitleBMJ Open
unibe.refereedtrue
unibe.subtype.articlecontribution

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