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Record W4214667364 · doi:10.3390/healthcare10030457

Understanding the Effects of Music Care on the Lived Experience of Isolation and Loneliness in Long-Term Care: A Qualitative Study

2022· article· en· W4214667364 on OpenAlexafffundabout
Sheetal Cheetu, Mara Medeiros-Domingo, Lauren Winemaker, Maggie Li, Lee Bartel, Bev Foster, Chelsea Mackinnon

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsDalhousie UniversityToronto Rehabilitation InstituteUniversity of TorontoMcMaster University
FundersOntario Trillium Foundation
KeywordsLonelinessSocializationGrounded theoryFlexibility (engineering)PsychologyQualitative researchIsolation (microbiology)Music therapyNursingLong-term careHealth careMedical educationMedicineSocial psychologySociologyPsychotherapist

Abstract

fetched live from OpenAlex

This qualitative study aims to understand the lived experience of residents and other stakeholders during the implementation of a comprehensive music program in long-term care. It was conducted using a subset of 15 long-term care homes from the Room 217 Foundation Music Care Partners (MCP) "Grow" study in Ontario, Canada. The MCP program's approach to music delivery uses therapeutic music practices such as "music care" to improve the care experience for caregivers and residents in long-term care homes. Thirty-two participants were interviewed, including staff, volunteers, and residents. Data were transcribed and analyzed using a modified grounded theory approach based on emergent themes. In total, seven themes arose from the data: limited resources, distinct experiences, life enrichment, dynamic relationships, program flexibility, potential continuity, and enhanced socialization. This study provides insight on barriers, enablers, and outcomes of the MCP program and on key considerations for implementing a novel interdisciplinary music program in a healthcare setting.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.215
GPT teacher head0.460
Teacher spread0.245 · 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 designQualitative
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

Citations6
Published2022
Admission routes3
Has abstractyes

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