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Record W2327361267 · doi:10.1386/ijcm.7.1.93_1

Discovering community music therapy in practice: Case reports from two Ontario hospitals

2014· article· en· W2327361267 on OpenAlexaffabout
Amy Clements-Cortés, Sarah Pearson

Bibliographic record

VenueInternational Journal of Community Music · 2014
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsWilfrid Laurier UniversityUniversity of WindsorUniversity of Toronto
Fundersnot available
KeywordsMusic therapyContext (archaeology)Action (physics)Health careVariety (cybernetics)PsychologyMedicineQuality (philosophy)NursingMedical educationPsychotherapistPolitical scienceHistory

Abstract

fetched live from OpenAlex

Abstract Music therapy holds a particularly valuable place in providing holistic health care, and medical settings are well suited to a community music therapy (CoMT) model of practice. As medicine continues to shift its focus to become preventative, health-promoting and patient-centred, the presence of live music in hospital environments can contribute to valuable collaborative relationships between members of the community who might not otherwise meet, while impacting and addressing patient wellness as well as patient illness. CoMT is a model music therapists are practicing within to focus on improving quality of life in a variety of domains for patients and families in various healthcare settings. This article explores two distinct community case reports from Ontario, Canada, in which an emerging CoMT practice fostered therapeutic collaborative relationships. Background information is provided on music therapy, performing, interdisciplinary health care and CoMT in context and action.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.697

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.004
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0040.003
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 head0.081
GPT teacher head0.399
Teacher spread0.319 · 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 designCase report
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

Citations10
Published2014
Admission routes2
Has abstractyes

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