Evaluating an Art Therapy Program in an Outpatient Psychiatric Hospital Setting for Individuals With Mood Disorders
Bibliographic record
Abstract
The aim of this program evaluation was to assess the impact of an 8-week art therapy intervention for adults in a hospital-based outpatient mood disorders clinic on depressive symptoms and overall quality of life and to examine how these symptoms may change over time by primary psychiatric diagnosis. Following a convergent mixed-methods approach, data collection included quantitative and qualitative patient feedback regarding program implementation to improve delivery. Pre- and post-treatment results from this evaluation are presented (n = 88), including patient feedback on the program (n = 34). Independent of primary diagnosis, patients experienced improvements in depressive symptoms (p < .001, ηp2 = .33), anxiety symptoms (p < .001, ηp2 = .16), and stress symptoms (p < 0.01, ηp2 = 15), as measured by the Depression Anxiety Stress Scales-21. In addition, patients experienced improvement in scores on the Quality of Life Enjoyment and Satisfaction Questionnaire—Short Form (p < .001, ηp2 = .37). Findings suggest that structured group art therapy can reduce symptoms of depression, anxiety, and stress and can improve quality of life in a Canadian outpatient psychiatric setting. Participants were generally satisfied with the quality of this service delivery and provided constructive qualitative feedback to help improve the service.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".