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Record W2315153824 · doi:10.1093/mtp/miv008

Adolescents’ Evaluation of Music Therapy in an Inpatient Psychiatric Unit: A Quality Improvement Project

2015· article· en· W2315153824 on OpenAlexaff
Michèle Preyde, A.S. Berends, Shrenik Parehk, John Heintzman

Bibliographic record

VenueMusic Therapy Perspectives · 2015
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsGrand River HospitalUniversity of Guelph
Fundersnot available
KeywordsMusic therapyUnit (ring theory)Quality (philosophy)MedicinePsychiatryPsychology

Abstract

fetched live from OpenAlex

Consistent with Quality Improvement initiatives in health care and at Grand River Hospital, a quality assessment of the music therapy program at the Child & Adolescent Mental Health unit was conducted. Medical Students administered the surveys in which youth were asked to rate their experiences with music in general on a 4-point scale, and specifically with this music therapy program on a 5-point scale. Youth were also asked to provide their perceptions of aspects of the music therapy they considered helpful, challenging or needed attention. This report is based on the 72 youth who participated in music therapy and completed an anonymous evaluation survey. The mean age of participants was 14.88 (SD 1.77), 48 (67%) identified as female, and most 57/66 or 86% reported some type of anxiety. Youth indicated that the music therapy was helpful for elevating their mood, reducing anxiety and in socially interacting with others. Suggestions for quality improvement are offered.

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)0.010
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.300
GPT teacher head0.458
Teacher spread0.159 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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