Accessing mental health care: A population-level exploration of the impact of immigration duration in the United States 2019–2023
Bibliographic record
Abstract
Immigrant populations in the United States are known to experience worsening mental health as time since immigration increases, with consistently lower rates of mental health service engagement compared to their domestic-born counterparts. However, there is little evidence investigating how time since immigration affects mental health service use. Using 2019-2023 National Health Interview Survey data, this population-based study examines how time since immigration influences use of mental health services among immigrants reporting monthly or more depression or anxiety symptoms among civilian, non-institutionalized adults in the United States. Of the 6,201 participants (representing 11.9 million adults annually), 21.2% reported accessing medication or counselling. Multivariable logistic regression analyses found that recent immigrants (<5 years) had 46% lower odds of receiving care compared to those residing in the United States for ≥5 years (95% CI: 0.38, 0.78). Sensitivity analyses lent robustness to study findings. Effect modification analyses revealed no significant variations in the relationship between time since immigration and mental health service use across citizenship status, symptom severity, and COVID-19 periods. Findings highlight the need for targeted interventions and policy reforms to address disparities in mental health service use among immigrants, promoting equitable access and improving well-being for recent immigrants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".