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Record W4383103156 · doi:10.1002/anzf.1547

A novel psychotherapy for low‐needs youth on the autism spectrum with emotional regulation challenges

2023· article· en· W4383103156 on OpenAlexaff
Everett McGuinty, Alain Carlson, J. Craig Nelson, Cailin Scott

Bibliographic record

VenueAustralian and New Zealand Journal of Family Therapy · 2023
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsBayer (Canada)Nipissing UniversityOntario Centre of Excellence for Child and Youth Mental Health
Fundersnot available
KeywordsAutismIntervention (counseling)PsychologyAutism spectrum disorderSession (web analytics)PopulationPsychotherapistProtocol (science)Clinical psychologyDevelopmental psychologyMedicinePsychiatryAlternative medicineComputer science

Abstract

fetched live from OpenAlex

Abstract The externalising of problems and implementation of interactive metaphors may improve emotional regulation of those clients presenting with autism spectrum disorder. This paper describes a new eight‐session treatment protocol in terms of using preferred interest metaphors with the strengths and strategies of client and family across the home, school, and community settings of client life. This exploratory treatment intervention uses externalising metaphors therapy as a brief treatment modality, addressing emotional regulation concerns of youth on the autism spectrum. The treatment model concretises affective states and creatively leverages visual strengths to improve this common presenting concern for this population. A case study on family therapy is presented with the protocol overview and illustrations. Further research is needed to address the testable hypotheses and identify the mediators of change resulting from this current model. This research would help to establish best practices in a clinical population for which there is no broadly accepted treatment paradigm. Mr. McGuinty has indicated that there are no conflicts of interest in this manuscript.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.167
GPT teacher head0.331
Teacher spread0.164 · 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 designTheoretical or conceptual
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

Citations3
Published2023
Admission routes1
Has abstractyes

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