Experiences of a Digital Mental Health Intervention from the Perspectives of Young People Recovering from First-Episode Psychosis: A Focus Group Study
Bibliographic record
Abstract
Horyzons is a digital health intervention designed to support recovery in young people receiving specialized early intervention services for first-episode psychosis (FEP). Horyzons was developed in Australia and adapted for implementation in Canada based on input from clinicians and patients (Horyzons-Canada Phase 1) and subsequently pilot-tested with 20 young people with FEP (Horyzons-Canada Phase 2). OBJECTIVE: To understand the experiences of young adults with FEP who participated in the pilot study based on focus group data. METHODS: Among the twenty individuals that accessed the intervention, nine participated across four focus groups. Three team members were involved in data management and analysis, informed by a thematic analysis approach. A coding framework was created by adapting the Phase 1 framework to current study objectives, then revised iteratively by applying it to the current data. Once the coding framework was finalized, it was systematically applied to the entire dataset. RESULTS: Four themes were identified: (1) Perceiving Horyzons-Canada as helpful for recovery; (2) Appreciating core intervention components (i.e., peer networking; therapeutic content; moderation) and ease of use; (3) Being unaware of its features; and (4) Expressing concerns, suggestions, and future directions. CONCLUSIONS: Horyzons-Canada was well received, with participants wanting it to grow in scale, accessibility, and functionality.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".