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Record W4393250585 · doi:10.59451/jami.40077

BREAKING FREE: HEALING PHYSICAL, VERBAL, AND SEXUAL ABUSE THROUGH THE BONNY METHOD OF GUIDED IMAGERY AND MUSIC

2014· article· en· W4393250585 on OpenAlexaff
Amy Clements-Cortés

Bibliographic record

VenueJournal of the Association for Music and Imagery · 2014
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsVerbal abuseSexual abusePsychologyGuided imageryApplied psychologyPoison controlHuman factors and ergonomicsMedicinePsychiatryMedical emergencyAnxiety

Abstract

fetched live from OpenAlex

The following case study is of Mary, a 29-year-old female, and her Bonny Method of Guided Imagery and Music (GIM) process covering ten sessions spanning over one year. Several concerns from Mary’s past surfaced in the GIM sessions, and Mary chose GIM to help her move past her issues of abuse. This paper provides background information on several subjects that were central to Mary's therapeutic process including adoption and consequent identity, and physical and sexual abuse. GIM sessions offered Mary the opportunity to break free from anger and resentment, and through the healing process she was able to grieve and prepare for the next transition in her life.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0010.001
Open science0.0000.003
Research integrity0.0010.001
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 head0.046
GPT teacher head0.358
Teacher spread0.311 · 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 designNot applicable
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

Citations8
Published2014
Admission routes1
Has abstractyes

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