Technology‐enabled collaborative care for youth with early psychosis: Results of a feasibility study to improve physical health behaviours
Bibliographic record
Abstract
AIM: Psychotic disorders are associated with excess morbidity and premature mortality. Contributing factors include tobacco smoking, low physical activity, and poor nutrition. This study tested a Technology-Enabled Collaborative Care model to improve health behaviours among youth with early psychosis. METHODS: A feasibility study among youth (ages 16-29) with early psychosis in Ontario, Canada. Participants were randomized to either a health coach supervised by a virtual care team (high intensity, n = 29), or self-directed learning (low intensity, n = 23) for 12 weeks. The primary outcome was participant engagement, defined as self-perceived benefit of changing health behaviours. Secondary outcomes were measures of health behaviours and programme-use metrics. RESULTS: Engagement was higher for high intensity participants for physical activity (adjusted group difference in change at 24 weeks = 3.4, CI95% = 1.9-4.9, p < .001) and nutrition (adjusted difference = 2.9, CI95% = 1.2-4.6, p = .001). No change was observed in health behaviours. Sixty two percent of participants completed 6 or more of the 12 weekly remote individualized health coaching sessions. Nine (39%) low intensity and 12 (41%) high intensity participants completed the final follow-up. CONCLUSIONS: Personalized health coaching for youth with psychosis is feasible and may have sustained benefits. However, retention with this population for 12 weeks is challenging.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".