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Record W4393243807 · doi:10.56883/aijmt.2023.87

The impact of group music therapy for individuals with eating disorders

2021· article· en· W4393243807 on OpenAlexaff
Priya Shah, Elizabeth Mitchell, Shannon Remers, Sherry Van Blyderveen, Heidi Ahonen

Bibliographic record

VenueApproaches An Interdisciplinary Journal of Music Therapy · 2021
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsWilfrid Laurier UniversityHomewood Research Institute
Fundersnot available
KeywordsEating disordersMusic therapyPsychologyGroup psychotherapyClinical psychologyPsychotherapistGroup (periodic table)

Abstract

fetched live from OpenAlex

This mixed-methods study examined the impact of group music therapy upon individuals receiving inpatient treatment for eating disorders. There was a total of 21 participants ranging between the ages of 16 and 58. Participants’ lived experiences of music therapy, including music’s effects on mood and emotion regulation, were explored. Data collected through the “PANAS” (Positive and Negative Affect Scale) (Watson et al., 1988), and subscales of the “DERS” (Difficulties in Emotion Regulation Scale) (Gratz & Roemer, 2004), and “ERQ” (Emotion Regulation Questionnaire) (Gross & John, 2003), demonstrated that participants experienced a decrease in negative affect, as well as an increased ability to express emotion after participating in music therapy. Data collected through audio recordings and transcriptions of music therapy and focus group sessions suggested that, through creating and playing music together, participants discovered music’s ability to represent various aspects of themselves and their recovery journeys, music’s potential to support them to externalise, shift, and stay with emotions, and music’s capacity to foster social connection.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.098
GPT teacher head0.381
Teacher spread0.284 · 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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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