Digital health innovation to prevent relapse and support recovery in young people with first-episode psychosis: A pilot study of Horyzons-Canada
Bibliographic record
Abstract
Digital health innovations may help to improve access to psychosocial therapy and peer support; however, the existence of evidence-based digital health interventions for individuals recovering from a first-episode psychosis (FEP) remains limited. This study aims to investigate the feasibility, acceptability, safety, and pre-post outcomes of Horyzons-Canada (HoryzonsCa), a Canadian adaptation of a digital mental health intervention consisting of psychosocial interventions, online social networking, and clinical and peer support moderation. Using a convergent mixed-methods research design, we recruited participants from a specialized early intervention clinic for FEP in Montreal, Canada. Twenty-three participants (mean age = 26.8) completed baseline assessments, and 20 completed follow-up assessments after 8 weeks of intervention access. Most participants provided positive feedback on general experience (85%, 17/20) and the utility of Horyzons for identifying their strengths (70%, 14/20). Almost all perceived the platform as easy to use (95%, 19/20) and felt safe using it (90%, 18/20). There were no adverse events related to the intervention. Participants used HoryzonsCa to learn about their illness and how to get better (65%, 13/20), receive support (60%, 12/20), and access social networking (35%, 7/20) and peer support (30%, 6/20). Regarding adoption, 65% (13/20) logged in at least 4 times over 8 weeks. There was a nonsignificant increase in social functioning and no deterioration on the Clinical Global Impression Scale. Overall, HoryzonsCa was feasible to implement and perceived as safe and acceptable. More research is needed with larger sample sizes and using in-depth qualitative methods to better understand the implementation and impact of HoryzonsCa.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".