S241. FACTORS ASSOCIATED WITH EARLY RISK OF DISENGAGEMENT FROM EARLY PSYCHOSIS INTERVENTION SERVICES
Bibliographic record
Abstract
Abstract Background Despite the body of evidence supporting early psychosis intervention (EPI) programs for young people with psychotic disorders, approximately 30% of individuals with first-episode psychosis disengage from care. To date, two factors, lack of family involvement and presence of a substance use disorder, have emerged as robust predictors of EPI disengagement. Several factors associated with service disengagement in mental health care more broadly have not been well-studied in EPI; some of these, such as homelessness and ethnicity, may be of particular importance to urban, multicultural populations, and ethnicity in particular has been shown to affect pathways into EPI services. Early missed appointments may signal risk for subsequent service disengagement. We sought to identify early predictors of disengagement risk in an urban EPI program. Methods We conducted a prospective chart review of consecutive patients accepted for services in a large, urban EPI program in Toronto, Canada in a 3-month period from July 4-October 3, 2018. Patients were observed in their first 3 months of treatment. The primary outcome of interest was risk of disengagement, defined as having missed at least 1 appointment without cancellation. Extracted data included a variety of demographic and clinical information. The principal investigator trained 2 data abstractors on the first 50 charts; subsequent agreement on the next 5 charts was 88%. Based on previous literature, we hypothesized that risk of disengagement would be increased in individuals with problem substance use, experiences of homelessness, and nonwhite race/ethnicity and decreased in individuals with family involvement in their care. We used logistic regression to examine the odds of disengagement associated with univariate predictors individually, and then together in a multivariate model. Results Seventy-three patients were consecutively admitted to EPI services in the 3-month period. Of these individuals, 59% (N=43) were identified as being at risk of disengagement based on having missed at least 1 appointment without cancellation. In the full sample, 71% (N=52) identified as nonwhite, 23% (N=17) had a documented experience of homelessness, 52% (N=38) had problem substance use, and 73% (N=53) had family involved in their care. In univariate logistic regression, only problem substance use was associated with risk of disengagement (OR=2.91, 95% CI 1.11–7.66); no significant associations were identified with race/ethnicity, experience of homelessness, or family involvement. In multivariate logistic regression, once we controlled for these other factors, the association between risk of disengagement and problem substance use was attenuated and no longer statistically significant (OR=2.15, 95% CI 0.77–5.97). Discussion In this small study of early disengagement in an urban EPI program, only problem substance use was associated with increased odds of missing an appointment, but not when we controlled for other factors thought to be associated with disengagement. Larger studies may be required to identify factors with small but important effects. These factors may be used to identify young people at risk of disengagement from EPI services early in care in order to target them for increased engagement efforts.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".