Virtual Delivery of Early Psychosis Care: Retrospective Cohort Study of Factors Associated With Initial Engagement
Bibliographic record
Abstract
Background: There is evidence that virtual delivery of early psychosis intervention (EPI) is well received by youth and has benefits such as reported improvements in accessibility, convenience, and comfort; however, potential barriers remain, including the digital divide and privacy concerns. Although initial engagement in EPI services is important for long-term recovery, little is known about initial engagement in the context of virtual care and the role of health equity and service use factors. Objective: This study aimed to identify factors associated with attendance at the initial EPI consultation appointment when most were being delivered virtually. Methods: This retrospective cohort study used electronic medical record data from patients aged 16 to 29 years who were referred to a large EPI program. The EPI program received 301 unique referrals that met study eligibility criteria from April to December 2020. Self-reported demographic variables were derived from the Centre for Addiction and Mental Health's structured health equity form and included age, gender, racial and ethnic group, country of birth, and sexual orientation. Service use factors derived from clinical documentation included referral source, days to consultation, and attendance at the consultation appointment, which was the primary outcome. Comparisons were made with 2018 to 2019 data from 999 participants from the same site prior to virtual care implementation using chi-square tests for categorical variables, independent t tests for continuous variables, and binary logistic regression. Results: Patients had a mean age of 23.2 (SD 3.3) years, and 214 (71.1%) participants identified as male. Compared with pre-virtual care, there were significantly higher rates of inpatient referral (114/301, 37.9%) and lower rates of referral from outpatient and other providers (122/301, 40.5%) post-virtual care (χ22=18.7, P<.001), with a small effect size and moderately narrow CI (Cramér V=0.120, 95% CI 0.06 to 0.17). Following univariable tests and stepwise backward selection, identifying as Black (odds ratio 0.45, 95% CI 0.21 to 0.97) and being referred from the emergency department or bridging clinic (odds ratio 0.24, 95% CI 0.08 to 0.72) were associated with decreased odds of attendance at the consultation appointment in the final adjusted model. All tests were 2-sided with an α level of .05. Conclusions: This study is innovative in that it examines the self-reported health equity and service use factors that may contribute to nonattendance when most EPI appointments are delivered virtually, unlike previous studies that focused solely on differences in attendance rates. Although it was during the COVID-19 pandemic and may not be representative of virtual care in real-world practice, this study suggests that virtual care may improve initial engagement in EPI services; however, barriers to care still exist for Black patients and those referred from the emergency department. A hybrid model may improve connection to EPI, though targeted approaches are needed to bridge the digital divide and ensure that structurally marginalized and high-acuity patients have equitable access to care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".