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Record W4378516076 · doi:10.3390/ijerph20105818

“Follow the Musical Road”: Selecting Appropriate Music Experiences for People with Dementia Living in the Community

2023· article· en· W4378516076 on OpenAlexafffund
Lisa Kelly, Amy Clements-Cortés, Bill Ahessy, Ita Richardson, Hilary Moss

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Toronto
FundersAlzheimer Society
KeywordsDementiaSingingPsychologyMusic therapyFocus groupExploratory researchMedicineApplied psychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

There are many music experiences for people with dementia and their caregivers including but not limited to individualized playlists, music and singing groups, dementia-inclusive choirs and concerts, and music therapy. While the benefits of these music experiences have been well documented, an understanding of the differences between them is often absent. However, knowledge of and distinction between these experiences are crucial to people with dementia and their family members, caregivers, and health practitioners to ensure a comprehensive music approach to dementia care is provided. Considering the array of music experiences available, choosing the most appropriate music experience can be challenging. This is an exploratory phenomenological study with significant Public and Patient Involvement (PPI). Through consultation with PPI contributors with dementia via an online focus group and senior music therapists working in dementia care via online semi-structured interviews, this paper aims to identify these distinctions and to address this challenge by providing a visual step-by-step guide. This guide can be consulted when choosing an appropriate music experience for a person with dementia living in the community.

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)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.174
GPT teacher head0.434
Teacher spread0.260 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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