M133. Comparing Early Intervention Service Disengagement Among Immigrants and Nonimmigrants
Bibliographic record
Abstract
Background: Objective: Early intervention (EI) programs invest significant resources in keeping clients engaged in treatment; however, disengagement remains a concern. It may be even more important for immigrant clients, yet it is unclear if particular service engagement efforts differentially impact certain immigrant subgroups. We therefore analyzed the rates and predictors of disengagement for immigrant vis-à-vis nonimmigrant clients in an EI setting. Methods: Two hundred ninety-seven clients were included in a time-to-event analysis with Cox Proportion Hazards regression models for all clients and for each immigrant sub-group. Immigrant status (categorized as first-generation immigrant, second-generation immigrant or nonimmigrant), age, gender, education level, substance abuse, family contact, social and material deprivation indices and 3-month medication non-adherence were included as predictor variables of service disengagement. Results: 24.2% (n = 72) of the 297 clients disengaged. There were no differences in disengagement rates between first-generation (23.3%), second-generation (22.7%) and nonimmigrants (25.3%). For all clients, medication non-adherence was the only statistically significant predictor of disengagement (HR = 3.81, 95% CI 2.37–6.14). For first-generation immigrants, age (HR = 1.17, 95% CI 1.02–1.34) and medication non-adherence (HR = 2.92, 95% CI 1.09–7.85) were significant predictors. For second-generation immigrants, material deprivation index (HR = 1.03, 95% CI 1.00–1.05) and medication non-adherence (HR = 11.07, 95% CI 3.20–38.22) were significant. Conclusion: Disengagement rates may be similar between first-generation, second-generation and nonimmigrants but the reasons for disengagement may be different. Disengagement is a complex process and a multi-pronged approach is needed to keep both immigrant and nonimmigrant clients engaged in EI programs. Dr. Veru is supported through a doctoral training award granted by the Fonds de recherché du Québec-Santé. Dr. Malla is supported by the Canada Research Chairs Program funded by the Federal Government of Canada. Dr. Iyer is funded by the Canadian Institutes of Health Research and Fonds de recherché du Québec-Santé. Dr. Shah receives funding from Fonds de recherché du Québec-Santé.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".