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Record W2758340715 · doi:10.1080/17454832.2017.1378241

Positive arts interventions: creative clinical tools promoting psychological well-being

2017· article· en· W2758340715 on OpenAlexaboutno aff
Olena Darewych, Nancy Riedel Bowers

Bibliographic record

VenueInternational Journal of Art Therapy · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsPsychological interventionMeaning (existential)PsychologyPsychotherapistArt therapyGlobeAestheticsSocial psychologyVisual artsArtPsychiatry

Abstract

fetched live from OpenAlex

Since ancient times, the arts have been used by humans across the globe as healing methods and vehicles for communication. The arts are increasingly becoming clinical tools to promote health and psychological well-being in individuals. This clinical paper features positive arts interventions which positive arts therapists, positive psychologists and positive psychotherapists can administer in their clinical practice for individuals of all ages to creatively tap into their imagination, reflect upon life goals, gain insight into their character strengths, activate their positive emotions, determine their sources of life meaning and explore spiritual avenues. The following positive arts interventions will be described in great detail: Scribble Drawing, My Strengths Collage, Bridge Drawing with Path (BDP), A Favourite Kind of Day (AFKD), Tree of Life, My Sources of Meaning and Spiritual Pathway. To illustrate these positive arts-based activities, the authors share clinical case examples from Australia, Canada and Ukraine.

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)0.002
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.219
GPT teacher head0.474
Teacher spread0.255 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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