“It is easier to express yourself through art”: A qualitative exploration of recovery through expressive arts
Bibliographic record
Abstract
Objective: Expressive arts (EA) is associated with positive experiences for those who engage in them. This study explores how PeaceLove, a structured EA program, contributes to personal recovery and well-being. Design: We used a qualitative inquiry to gain a multifaceted perspective of personal recovery through participation in PeaceLove program. Setting: This study was conducted at one of the four standalone specialty psychiatric hospitals in Ontario—Canada’s most populous province. The 346-bed hospital offers a range of specialized mental health services to those living with complex and serious mental illness, including adolescent, adult, forensic, and geriatric patients. Main outcome measure(s): Four focus group interviews were conducted with 16 individuals with chronic mental health conditions to explore how PeaceLove promoted their personal recovery goals. Results: The mean age of participants was 44 years with 56 percent being female. Analysis revealed four interconnected themes—self-discovery, recovery, art as a medium of expression, and a sense of well-being—contributing to participants’ personal recovery journey. Conclusion: The study findings highlight the benefits of EA on individuals’ personal recovery and recommend recreational therapists to use EA as a “recovery tool” to support individuals in their recovery journey.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".