Service Engagement in Virtually Delivered Psychosis Treatment: A Systematic Review and Mixed Methods Evaluation
Bibliographic record
Abstract
Disengagement remains a significant dilemma within mental healthcare, and among conditions characterized by psychosis specifically. Traditionally delivered in-person, specialized psychosis services, including early psychosis intervention (EPI), have promptly transitioned to virtual delivery amid the COVID-19 pandemic, despite limited research on engagement in virtual psychosis care. This thesis consists of a systematic review and mixed methods evaluation; the latter examining rates, predictors, and experiences of (dis)engagement within a virtual EPI program. Electronic health records of a cohort of EPI patients enrolled between April-November 2020 were examined; individual interviews and focus group discussions were conducted with patients, family members, and clinicians. Approximately 15.5% of the sample formally disengaged by follow-up (15-24 months); 12.3% disengaged within the first 9 months. Disengagement was associated with lack of early use of SEE (HR=.28, 95% CI=.12-.67) and NEET (HR=3.04, 95% CI=1.03-8.98). Qualitative analysis revealed 5 salient themes and service recommendations, with most preferring a hybrid model.
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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 | 0.082 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".