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Record W4395050310 · doi:10.3390/bs14050354

Music Therapy Assessment for Older Adults: Descriptive Mixed-Methods Study

2024· article· en· W4395050310 on OpenAlexaffabout
Amy Clements-Cortés

Bibliographic record

VenueBehavioral Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMusic therapyDescriptive statisticsPsychologyTest (biology)Applied psychologyClinical psychologyMedical educationMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Background: The purpose of this inquiry was to test the new ‘Music Therapy Assessment for Older Adults’ (MTAOA) tool in Canada and the United States, and to establish its content and predictive utility. Methods: A pilot study using an explanatory descriptive methods design was chosen; n = 18 music therapists completed an online survey about their experiences in administering the assessment and 50% (n = 9) were invited for a follow-up interview. Results: The results indicated that the MTAOA was a beneficial assessment tool that contained relevant domains (89%) to develop a music therapy treatment plan; 89% of music therapists also noted they would continue to use and recommend the MTAOA. The data produced beneficial information that were used to revise the assessment form to ensure inclusive language and reduce any potential inherent or unconscious biases. Conclusions: Future research is needed to assess the utility of the revised MTAOA in other global regions where music therapists work with 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.190
GPT teacher head0.546
Teacher spread0.356 · 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 designOther design
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

Citations3
Published2024
Admission routes2
Has abstractyes

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