Arts and the media – Space 22
Bibliographic record
Abstract
Television broadcasting has had a longstanding commitment to educating the public on mental health issues; however, ‘mental health intervention television’ which involves members of the public taking part in made-for television interventions is a more recent development. This type of programming focuses on intervening in the lives of the cast with the goal of making a positive impact on mental health and wellbeing. This study describes Space 22 as an exemplar of a mental health intervention on television. Space 22 is a six-part television series exploring the impact of arts engagement on mental health using mixed methods. Seven participants with lived experience of mental health challenges engaged in art workshops, guided by professional artists. Quantitative measures included the Warwick-Edinburgh Mental Health and Wellbeing Scale (WEMWBS), Short Mood Scale (SMS), Toronto Empathy Questionnaire (TEQ), and the Social Inclusion Scale. Results showed significant improvements in wellbeing scores, with a mean increase of 14.4 points on the WEMWBS, exceeding the threshold for meaningful clinical change. Qualitative analysis identified ‘connectedness’ as the dominant theme through four dimensions: connection to (i) group members, (ii) self, (iii) public advocacy, and (iv) the creative process. The study demonstrates the potential of television-based arts interventions for enhancing mental health, particularly through fostering social connections and enabling public mental health advocacy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".