Language ability and virtual mental healthcare utilisation among immigrant and refugee youth: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The widespread adoption of virtual care during the pandemic may not have been uniform across populations, including among paediatric immigrants and refugees. We sought to examine the association between virtual mental healthcare utilisation and immigration factors. METHODS: This population-based cohort study of immigrants and refugees (3-17 years) used linked health administrative databases in Ontario, Canada (March 2020 to December 2021). Exposures included self-reported Canadian language ability (CLA) at arrival and immigration category (economic class, family class and refugee). The primary outcome was the visit modality (inperson/virtual) measured as a rate of physician-based mental healthcare visits. Modified Poisson regression model estimated adjusted rate ratios (aRRs) with 95% CIs. RESULTS: Among 22 420 immigrants, 12 135 (54%) did not have CLA (economic class: 6310, family class: 2207, refugees: 3618) and 10 285 did (economic class; 6293, family class: 1469, refugees: 2529). The cohort's mean age (SD) was 12.0 (4.0) years and half (50.3%) were female. Of 71 375 mental health visits, 47 989 (67.2%) were delivered virtually. Compared with economic class immigrants with CLA (referent), refugees with and without CLA had a lower risk of virtual care utilisation (CLA: aRR 0.89, 95% CI 0.86 to 0.93; non-CLA: aRR 0.80, 95% CI 0.77 to 0.83), as did family class immigrants with CLA (aRR 0.96, 95% CI 0.92 to 0.99). No differences in virtual care utilisation were observed among economic class immigrants with CLA and other immigrant groups. CONCLUSIONS: Language ability at arrival and immigration category are associated with virtual mental healthcare utilisation. Whether findings reflect user preference or inequities in accessibility, particularly for refugees and those without CLA at arrival, warrants further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".