Online art therapy in elementary schools during COVID-19: results from a randomized cluster pilot and feasibility study and impact on mental health
Bibliographic record
Abstract
BACKGROUND: Emerging literature on the current COVID-19 crisis suggests that children may experience increased anxiety and depression as a result of the pandemic. To prevent such school and mental health-related problems, there is a timely need to develop preventive strategies and interventions to address potential negative impacts of COVID-19 on children's mental health, especially in school settings. Results from previous child clinical research indicate that art-based therapies, including mindfulness-based art therapy, have shown promise to increase children's well-being and reduce psychological distress. OBJECTIVE: The goal of the present pilot and feasibility study was to compare the impact of an emotion-based directed drawing intervention and a mandala drawing intervention, on mental health in elementary school children (N = 22), in the context of the COVID-19 pandemic. Both interventions were group-based and delivered online and remotely. A pilot study using a randomized cluster design was implemented to evaluate and compare both interventions in relation to child anxiety, depression, inattention and hyperactivity symptoms. RESULTS: Analyses of covariance revealed a significant effect of the type of drawing intervention on levels of inattention, after controlling for baseline levels. Participants in the emotion-based directed drawing group showed lower inattention scores at post-test, when compared to participants in the mandala group. Post-hoc sensitivity analyses showed significant decreases in pre-to-post scores for levels of hyperactivity for the complete sample. CONCLUSION: Overall, results from this pilot and feasibility study showed that both an emotion-based directed drawing intervention and a mandala drawing intervention may be beneficial to improve mental health in elementary school children, in the context of the current COVID-19 pandemic. From a feasibility standpoint, results indicate that the implementation of both interventions online and remotely, through a videoconference platform, is feasible and adequate in school-based settings. Further work incorporating larger sample sizes, longitudinal data and ensuring sufficient statistical power is warranted to evaluate the long-term impact of both interventions on children's mental health.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".