Videoconference‐led art‐based interventions for children during COVID‐19: Comparing mindful mandala and emotion‐based drawings
Bibliographic record
Abstract
Abstract Emerging evidence on the coronavirus disease 2019 (COVID‐19) pandemic suggests that children are experiencing a deterioration in mental health, namely, an increase in anxiety, depression, and hyperactivity symptoms. To address this rising issue, preventive strategies and mental health interventions need to be evaluated to help children in their school setting. Recent studies have suggested that art‐based interventions could increase children's well‐being and be easily implemented in schools. The goal of this study was to assess the effects of an emotion‐based directed drawing intervention, compared to a mandala drawing intervention, on elementary school children's ( n = 165) mental health, in the context of the COVID‐19 pandemic. An experimental design was used to compare the effects of the two interventions on primary school students' anxiety, depression, and inattention symptoms. All drawing activities were led by an online facilitator, while children and teachers attended school in‐person. Mixed analyses of variance revealed a significant effect of time on students' levels of anxiety. Post hoc sensitivity analyses indicated that children from both groups reported lower levels of anxiety pre‐ to postintervention. Results from this study showed that, in the context of the COVID‐19 pandemic, both emotion‐based and mandala drawing interventions could improve certain mental health aspects of elementary school children, by reducing their anxiety levels. Informal evidence indicates that implementing these interventions online and remotely through a videoconferencing platform is feasible and well received by children and their teachers. Nevertheless, future studies should include an inactive control group, explore the acceptability of the intervention, and use longitudinal methods to better document if the positive impacts on mental health can be maintained through time.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".