<scp>Technology‐enabled</scp> collaborative care for youth with early psychosis: A protocol for a feasibility study to improve physical health behaviours
Bibliographic record
Abstract
AIM: Individuals with psychotic disorders have poorer health outcomes and die earlier due to cardiovascular diseases when compared to healthy populations. Contributing factors include low levels of physical activity, poor nutrition and tobacco smoking. Currently, patients navigate a fragmented health-care system to seek physical and mental health services, often without access to evidence-based health promotion interventions, especially in non-academic settings or rural areas, increasing client barriers at the individual and provider level. To address these gaps, we wish to test the feasibility and impact of a Technology-Enabled Collaborative Care for Youth (TECC-Y) model to improve healthy behaviours among youth with early psychosis. The model addresses geographical barriers and maldistribution of physical and mental health care. METHODS: A randomized controlled trial, including youth (ages of 16-29) with early psychosis (diagnosed in the past 5 years) residing in Ontario, Canada. Our primary outcome is client engagement. Secondary outcomes include smoking status, physical health and nutrition. Participants are randomly assigned to either a health coach supervised by a virtual care team, or a self-directed learning group (e-platform with psychoeducational materials). Assessments are conducted at baseline, 6, 12 and 24 weeks. RESULTS: This paper presents the protocol of the study. Recruitment commenced in August 2018. This study was registered on 16 July 2018 on clinicaltrials.gov (Registry ID: NCT03610087). CONCLUSIONS: TECC-Y will determine if a technology-based collaborative care model engages youth with early psychosis, and whether this will be associated with changes in smoking, physical health and nutrition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".