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Record W4240780526 · doi:10.1093/geroni/igz038.2760

THE MAGIC OF MUSIC FOR IMPROVING PSYCHOLOGICAL HEALTH FOR THOSE WITH DEMENTIA AND THEIR CAREGIVERS

2019· article· en· W4240780526 on OpenAlexaff
Stuart MacDonald, Debra Sheets, André Smıth, Sandra R. Hundza

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDementiaPsychological interventionIntervention (counseling)PsychologyCognitionClinical psychologyNeuropsychologyChoirPsychotherapistMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Arts-based interventions for person’s with dementia and their caregivers represent an inexpensive, non-invasive, and non-pharmacological intervention with the potential to improve psychological function as well as reduce healthcare costs. The paper presents an overview of Voices in Motion (ViM), and the impact of this social-cognitive intervention on changes in psychological function for those with dementia and their caregivers (current n=26 dyads). Choir rehearsals were held on a weekly basis, and included a social discussion component. A range of outcomes (neuropsychological and physiological function, neural activation) were assessed using an intensive repeated measures design that facilitates both between- and within-person analyses, including nuanced evaluation of whether psychological function improves post intervention relative to an individual’s personal average, yielding a conservative within-person test of the benefits of intervention. Discussion focuses on the promise of such interventions for mitigating dementia symptoms and facilitating the psychological health of caregivers.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.060
GPT teacher head0.371
Teacher spread0.311 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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