Virtual Delivery of Early Psychosis Care: A Retrospective Cohort Study of Factors Associated with Initial Engagement (Preprint)
Bibliographic record
Abstract
BACKGROUND The transition to virtual care delivery following the COVID-19 pandemic had the potential to impact access to and engagement with early psychosis intervention (EPI) services. Despite evidence that virtual EPI is well-received by youth and has benefits such as reported improvements in accessibility, convenience, and comfort, there remain potential challenges with technology including the amplification of the “digital divide” and privacy or confidentiality concerns. Early engagement in EPI services is important for long-term recovery; however, little is known about EPI engagement in the context of virtual care. Our previous work showed that older patients and those referred from the emergency department (ED) were less likely to attend their EPI consultation appointment, but it is not clear how these and other factors impact engagement in virtual care. OBJECTIVE 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 who were referred to a large EPI program between April 2020 and December 2020. The primary outcome was the rate of attendance at the EPI consultation appointment. Variables included health equity and service use factors. Statistical comparisons were made with 2018-2019 data from the same site prior to virtual care implementation using chi-square tests for categorical variables and independent t tests for continuous variables, as well as binary logistic regression. RESULTS Between April and December 2020, 301 unique patients were referred for EPI. Patients had a mean (standard deviation) age of 23.2 (3.3) years; 214 (71.1%) identified as male; 88 (29.2%) identified as White; 121 (40.2%) identified as heterosexual; 139 (46.2%) were born in Canada. Compared to pre-virtual care, the proportion of inpatient referrals was higher (114/301, 37.9%), while referrals from outpatient and other providers were lower (122/301, 40.5%) post-virtual care (χ22=18.7, P<.001). The wait time from referral to consultation decreased post-virtual care (t1,149=6.44, P<.001). Attendance at the consultation appointment increased post-virtual care (84.1%, 253/301) compared to pre-virtual care (77.1%, 770/999) (χ21=6.71, P=.01, φ=0.072). In the multivariable model, patients identifying as Black (OR 0.45, 95% CI 0.21-0.96) and patients referred from the ED or bridging clinic (OR 0.23, 95% CI 0.08-0.69) had decreased odds of attendance at the consultation appointment. CONCLUSIONS Findings from this cohort study of patients referred to EPI services suggests that virtual care may improve initial engagement in EPI services; however, barriers to care still exist for structurally marginalized and high acuity patients.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".