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Record W4221165462

The therapeutic value of creative art-making during the covid-19 pandemic

2022· article· en· W4221165462 on OpenAlexaff
Helen W. CHAN, Angelie Ignacio, Clara B. Rebello, Gerald C. Cupchik

Bibliographic record

VenueDergiPark (Istanbul University) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Value (mathematics)VirologyMedicineComputer scienceDiseaseInfectious disease (medical specialty)Pathology
DOInot available

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has been a major life stressor and building resilience is integral to coping with it. Art-making is one way to address the adversities of the pandemic as it allows creative individuals to experience positive affect, engage in self-reflection, and heal psychological wounds. In this study, 270 participants completed a background survey reflecting upon health and precautionary measures, emotional state felt prior to participating, and trait resilience. Participants also assessed their artistic practices both before and during the pandemic with the focus on change in attitudes. Each described an artwork created during the pandemic and reflected on its value. As expected, participants who followed precautionary measures were in better health, experienced positive affect, and were generally more resilient. Emotional self-care became a primary focus of art-making during the pandemic, whereas getting into a state of flow and having a non-judgmental attitude while creating the artwork were the central focus prior to the pandemic. These findings show that art-making offers therapeutic benefits for an individual’s psychological well-being and that there were deleterious impacts of the pandemic on the self-expression process.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.907
Threshold uncertainty score0.999

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 studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.264
Teacher spread0.214 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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