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Record W4225007655 · doi:10.4324/9781003149538-18

Reducing Anxiety Levels During a Pandemic with Virtual Art Therapy

2022· book-chapter· en· W4225007655 on OpenAlexaboutno aff
Hedaya AlDaleel, Haley Toll, Michelle Winkel, Christel Bodenbender

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyLikert scaleSession (web analytics)Exposure therapyPsychologyClinical psychologyCoronavirus disease 2019 (COVID-19)PandemicMedicineDevelopmental psychologyPsychiatryComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

The quasi-experimental pre-post study presented in this chapter examines the use of virtual individual art therapy for the alleviation of anxiety of 87 Canadian and international adult clients (over 18 years of age). From May 2020 to May 2021, student art therapists at the Canadian International Institute of Art Therapy used three Likert ten-point scale questions to measure changes in their clients’ anxiety levels before and after virtual art therapy sessions. The study showed a 36% reduction in anxiety after the art therapy session when comparing mean anxiety scores at the beginning of the session to the end. When comparing the anxiety score from the past week to the end of the session, anxiety was lowered even further by 45% at session end. Furthermore, there was a smaller reduction of 14% between the mean past week score and the score at the beginning of the session. Although these results are preliminary and contain a small sample size, they are encouraging and should be expanded upon with a longer-term and large-scale study.

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)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.066
GPT teacher head0.251
Teacher spread0.185 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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