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Record W4309962701 · doi:10.1016/j.heliyon.2022.e11894

Characteristics of music intervention to reduce anxiety in patients undergoing cardiac catheterization: scoping review

2022· article· en· W4309962701 on OpenAlexaboutno aff
Letícia de Carvalho Batista, Michele Nakahara Melo, Diná de Almeida Lopes Monteiro da Cruz, Rita de Cássia Gengo e Silva Butcher

Bibliographic record

VenueHeliyon · 2022
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPsycINFOCINAHLPsychological interventionScopusAnxietyMEDLINEMedicineCardiac catheterizationMusic therapyPhysical therapyNursingSurgeryPsychiatry

Abstract

fetched live from OpenAlex

The characteristics of music interventions for reducing anxiety in patients undergoing cardiac catheterization were mapped. A scoping review was conducted according to the Joanna Briggs Institute methodology. Searches were performed in electronic portals and databases PubMed, CINAHL, PsycINFO, Cochrane, EMBASE, Scopus, LILACS, CAPES Thesis Portal (Brazil), DART-Europe E-theses Portal, Theses Canada Portal, Pro-Quest, and Google Scholar databases, gray literature, with no limitation on the year of publication. Eighteen articles were included in the search. The characteristics of the interventions were heterogeneous and not comprehensively described in the primary studies. The songs were predominantly of a single genre, instrumental, and selected by the interventionist, with a rhythm between 60 and 80 beats per minute. The interventions were delivered in a single session, mostly in the catheterization laboratory, before or during the procedure, by means of digital audio and earphones for over 20 min. The heterogeneity of interventions and incompleteness of information in the studies compromises the advancement of knowledge on the effects of music on health outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.042
GPT teacher head0.349
Teacher spread0.308 · 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.

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

Citations12
Published2022
Admission routes1
Has abstractyes

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