The therapeutic value of creative art-making during the covid-19 pandemic
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".