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Record W4229011149 · doi:10.5539/gjhs.v14n6p1

Nurses’ Self-Efficacy Regarding Cardiopulmonary Resuscitation: A Literature Review

2022· review· en· W4229011149 on OpenAlexvenueno aff
Ayman Ateq Alamri, Omar Ghazi Baker

Bibliographic record

VenueGlobal Journal of Health Science · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsCardiopulmonary resuscitationSelf-efficacyMedicineNursingResuscitationAnxietyAction (physics)Intensive care medicinePsychologyEmergency medicinePsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Early detection and effective resuscitation response are critical to the survival of cardiac arrest patients. Nursing staff are frequently the initial responders to cardiac arrest patients. The level of self-efficacy lessens the anxiety of new nursing graduates and enhances their ability to do cardiopulmonary resuscitation (CPR). This paper aimed to investigate the relationship between self-efficacy and nurses’ abilities regarding CPR. Self-efficacy refers to “beliefs in one’s capacity to arrange and execute the courses of action required to achieve specific attainments”. Two main categories discussed in this study include self-efficacy of nurses about CPR and factors affecting their self-efficacy. As a result, nurses must have the essential knowledge and attitude, and trust in their self-efficacy to provide adequate nursing care to cardiac patients. Furthermore, nurses need to be prepared regarding CPR’s knowledge and self-efficacy before delivering it, since these are crucial aspects that affect CPR delivery.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.402
Teacher spread0.365 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

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Same venueGlobal Journal of Health ScienceSame topicCardiac Arrest and ResuscitationFrench-language works237,207