Recovery After Psychosis: Qualitative Study of Service User Experiences of Lived Experience Videos on a Recovery-Oriented Website
Bibliographic record
Abstract
BACKGROUND: Digital interventions offer an innovative way to make the experiences of people living with mental illness available to others. As part of the Self-Management And Recovery Technology (SMART) research program on the use of digital resources in mental health services, an interactive website was developed including videos of people with lived experience of mental illness discussing their recovery. These peer videos were designed to be watched on a tablet device with a mental health worker, or independently. OBJECTIVE: Our aim was to explore how service users experienced viewing the lived experience videos on this interactive website, as well as its influence on their recovery journey. METHODS: In total, 36 service users with experience of using the website participated in individual semistructured qualitative interviews. All participants had experience of psychosis. Data analysis occurred alongside data collection, following principles of constructivist grounded theory methodology. RESULTS: According to participants, engaging with lived experience videos was a pivotal experience of using the website. Participants engaged with peers through choosing and watching the videos and reflecting on their own experience in discussions that opened up with a mental health worker. Benefits of seeing others talking about their experience included "being inspired," "knowing I'm not alone," and "believing recovery is possible." Experiences of watching the videos were influenced by the participants' intrapersonal context, particularly their ways of coping with life and use of technology. The interpersonal context of watching the videos with a worker, who guided website use and facilitated reflection, enriched the experience. CONCLUSIONS: Engaging with lived experience videos was powerful for participants, contributing to their feeling connected and hopeful. Making websites with lived experience video content available to service users and mental health workers demonstrates strong potential to support service users' recovery.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".