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Record W4402326552 · doi:10.1177/07334648241275965

Exploring the Therapeutic Effects of Music Intervention Embedded With Binaural Beats on Health and Well-Being of Older People: A Scoping Review

2024· review· en· W4402326552 on OpenAlexaff
Onouma Thummapol, Sadaf Murad, Oluwakemi Amodu, Megan Kennedy

Bibliographic record

VenueJournal of Applied Gerontology · 2024
Typereview
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychological interventionContext (archaeology)Intervention (counseling)Music therapyPsychologySystematic reviewRehabilitationMedicineInclusion (mineral)MEDLINEGerontologyPsychotherapistNursingPhysical therapy

Abstract

fetched live from OpenAlex

The utilization of music intervention featuring auditory binaural beats (BBs) has garnered attention as a promising avenue for enhancing the health and well-being of younger, healthy individuals. This scoping review systematically examines the effects and correlates associated with BB stimulation in the context of older adults' health. Additionally, it briefly addresses how incorporating BBs as a therapeutic modality can facilitate medical treatment strategies and support the rehabilitation of aging populations. Employing scoping review methodology, and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension (PRISMA-ScR) for Scoping Review guidelines, a comprehensive literature search of seven databases was conducted. Twelve articles meeting the predefined inclusion criteria were identified and subsequently incorporated into the review. The findings of this scoping review underscore a notable paucity of studies exclusively dedicated to investigating the innovative and noninvasive application of binaural beat interventions among older individuals. The review delves into the applications of BB stimulation, health outcomes, and factors influencing the efficacy of BB interventions, with a particular focus on the older adults.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.720
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.133
GPT teacher head0.423
Teacher spread0.290 · 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 designSystematic review
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

Citations2
Published2024
Admission routes1
Has abstractyes

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