Expressive Arts for Grieving Youth: A Pilot Project
Bibliographic record
Abstract
The experience of loss due to death, illness, and social mitigation was inevitable during the COVID-19 pandemic. Mental health services are chronically difficult to access in Canada, and this barrier is further exacerbated when trying to access certified art therapists to deliver expressive arts therapy. This pilot project attempted to provide an alternative to this service through an interprofessional alliance with a professional artist and certified counselors. A small group (n = 6) of vulnerable youth who had suffered the recent loss of a loved one and were at risk for mental health issues participated in an expressive arts therapy program, over a four-week period in the late Spring of 2021. Expressive arts activities such as clay mask making to express the emotions of grief, provided opportunities for the youth to learn healthy ways of coping with grief and loss. A mixed-methods approach involving quantitative data was collected with a battery of well-validated instruments to assess changes in depressive symptomatology, social and emotional loneliness and satisfaction with life. These measures were complemented with qualitative data collected during a focus group at the end of the program. Measures conducted before and after the program found decreases in loneliness, coupled with youth expressing the shared experience was comforting, reduced feelings of isolation, and increased a sense of belonging. Preliminary evidence supports that expressive arts programs for vulnerable youth may help to stabilize mood, decrease feelings of isolation/loneliness, and may generate a supportive community of peers, providing a safe space for the expression of grief through creative outlets.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".