Impact on public attitudes of a mental health audio tour of the National Gallery in London
Bibliographic record
Abstract
AIM: The arts have the potential to increase public awareness about mental health and reduce stigma. However, arts-based projects to raise awareness have been small-scale. In this study, a mental health-awareness audio tour of The National Gallery in London was co-produced and narrated by young adults with relevant lived experience. The study investigated the acceptability of the tour to the public and evaluated its impact on public attitudes about mental health. METHODS: Participants were Gallery visitors over four consecutive days. The tour led visitors on 10 stops through the Gallery. Each stop focused on artworks and Gallery spaces, challenged common myths about mental health, and invited visitors to consider their personal views. Participants completed measures of mood and attitudes about mental health pre- and post-tour and provided narrative feedback. RESULTS: Pre-tour, participants (N = 213) reported high levels of happiness, compassion towards people with mental health conditions, comfort talking about mental health, and positive attitudes about mental health. Post-tour, participants (N = 111) reported significant increases in happiness, comfort, and positive attitudes. In feedback, participants (N = 85) reported that strengths of the tour were the music, inclusion of lived experience, art and mental health links, and reported that the tour was informative, innovative, and improved mental health awareness. CONCLUSIONS: The tour increased positive attitudes, despite positive baseline attitudes, indicating the feasibility of arts-based interventions in major venues to reduce stigma. Sampling limitations and participant retention suggest that arts-based projects to raise awareness should target more diverse audiences and consider data collection strategies in large venues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".