The relationship between neighbourhood income and youth mental health service use differs by immigration experience: analysis of population-based data in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: We investigated the relationship between neighbourhood income quintile and mental health service use by immigration experience among youth and explored changes during the COVID-19 pandemic. METHOD: We used administrative data to examine mental health service use among youth aged 10 to 24 in British Columbia, Canada, between April 1, 2019, and March 31, 2022. We compared rates of community-based mental health service use, emergency department visits, and hospitalizations and the proportion of involuntary admissions by neighbourhood income quintile and immigration. We used models stratified by immigration to estimate the relationship with income. RESULTS: Non-immigrant youth used substantially more services than immigrant youth. Service use increased following the pandemic's start and peaked between January and March 2021. We observed a clear income gradient for community-based service use among both immigrant and non-immigrant youth, but the direction of the gradient was reversed. Service use was highest among non-immigrant youth in lower-income neighbourhoods and lowest for immigrant youth in lower-income neighbourhoods. We observed similar patterns of income gradient for non-immigrant youth for emergency department visits and hospitalization. The proportion of involuntary admissions was higher for immigrant youth. CONCLUSIONS: Mental health service use was substantially lower among immigrant youth than non-immigrant youth, but higher proportions of immigrant youth were hospitalized involuntarily. The reverse income gradient patterns observed for community-mental health service use are noteworthy and suggest significant barriers to accessing preventable care among immigrant youth, particularly those living in lower-income neighbourhoods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".