Artway: gallery art therapy group for young people with mental health difficulties
Bibliographic record
Abstract
Background Research suggests that art therapy in museums and galleries can be beneficial to mental wellbeing, but there has been little research in this field relating to young people. There is a need to understand how an art gallery context might contribute to such effects.Aims: We aimed to understand mechanisms of therapeutic change (Springham and Huet 2018), where it could be evidenced, when participants with mental health challenges made, looked at and discussed art together in an art gallery.Methods Three eight-week art therapy groups were delivered for young people at a gallery alongside a professional artist. Fifteen participants completed standardised pre- and post-outcome measures to contextualise the theorising. Video recordings of sessions were analysed by two art psychotherapists in consultation with Author 2. We used grounded theory methodology to develop a theory about what processes were happening during the sessions.Results There was a statistically significant change on self-reported wellbeing from beginning to end of the intervention for participants as a group, but not on the self-esteem measure. The developed theory describes the way the gallery context and working with an artist appeared to enhance and change the varying focus of an art therapy group.Conclusion: The developed theory goes some way to understanding the mechanisms of change in an art therapy group for young people in a gallery.Implications for practice/policy/future research Contemporary art venues and working alongside professional artists can offer a stimulating environment for therapeutic change in art therapy groups. Further research is needed to develop the theory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".