Parents’ Perspectives of an Arts Engagement Program Supporting Children with Anxiety
Bibliographic record
Abstract
Arts engagement programs (AEPs) are non-clinical, structured programs led by artists and educators to support mental health and wellbeing. While evidence demonstrates positive mental health outcomes in adult AEPs, studies of childhood AEPs remain sparse. We created a gallery-based AEP (Culture Dose for Kids) for children with anxiety based on a successful arts engagement pilot for adults with depression. We questioned whether our tailored-for-children adult program would effectively and feasibly support children's mental health. Through parents' perspectives and feedback, this study tested the program's acceptability, feasibility, and effectiveness with children with anxiety. Quantitative and qualitative measures were used to determine whether the program was an effective and acceptable mental health support for children with anxiety. Our findings revealed that the program positively and significantly impacted parental perceptions of their child's anxiety. Our findings illustrate depictions of improved mood, confidence, and sense of empowerment in the child, qualities associated with resilience and mental wellbeing. Open-ended activities provided opportunities for connection, creativity, and experimentation-sources of strength for improving mental health. This study adds to the small but growing evidence base supporting the role of arts-based community care in youth mental health and wellbeing.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".