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Record W2787229597 · doi:10.47513/mmd.v10i1.568

Using music and medicine research to inform music psychotherapy practice

2018· article· en· W2787229597 on OpenAlex
Heidi Ahonen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMusic and Medicine · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMusic therapyPsychologyPsychotherapistFoundation (evidence)Key (lock)MusicologyPower (physics)Music psychologyMusic and emotionMusic historyMusic educationPedagogyComputer scienceHistory

Abstract

fetched live from OpenAlex

This article describes some key elements that continue impacting my personal journey from qualitative researcher and music psychotherapist into evidence-based researcher. I will contemplate how to explain the power of music within the framework of music and medicine, and introduce the relevant music and brain research findings, i.e. how music affects our hormones, emotions, and memories. Could I have a neurological rationale in my mind when I choose or use music for my music psychotherapy clients? Do the music and brain findings expound what exactly in music is therapeutic and what happens during those processes? Could we speculate if indeed those findings are the very foundation of music psychotherapy?

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.389
GPT teacher head0.490
Teacher spread0.101 · 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