Essays on Immigrant Mental Health: A Look at Health Reporting, Model Identification, and Policy Changes
Bibliographic record
Abstract
Upon arrival to the host country, immigrants are, on average, physically healthier than the native-born population; however, it is unclear whether this health benefit extends to mental health or whether it applies to recent immigrant cohorts. This thesis seeks to understand the healthy immigrant effect with regards to mental health, by examining aspects of mental health reporting and immigrant mental health using both survey and administrative health data. The first chapter explores the potential role of stigma in mental health reporting by combining survey data, which may be subject to reporting biases, and administrative health data, an alternative source of data, which may be more objective. Results suggest that underreporting may be highest for the most stigmatizing conditions, such as schizophrenia and self-harm, followed by mood disorders, and lowest for physical health conditions, such as cancer and diabetes. The second chapter attempts to disentangle the role of length of stay, period, and cohort in explaining the healthy immigrant effect in mental health as measured by poor mental health and the presence of mood and/or anxiety disorders. Accounting for length of stay, period, and cohort effects is necessary to avoid biased results. Using orthogonal polynomial parameterization in the model, results suggest that period effects are insignificant and thus do not play a role in explaining immigrant mental health. Furthermore, mental health appears to decline the longer immigrants remain in Canada in the survey data, but not in the administrative data. These data sources may be measuring different aspects of health and health care utilization. The third chapter explores the presence of cohort effects in immigrant mental health by evaluating the impact of three policy changes associated with increasingly strict language requirements and applied differentially to cohorts of recent immigrants in each visa class. Results show that mental health care utilization decreased for immigrants who were most impacted by the policy compared to long-term residents, particularly following policies that coincided with periods of increased English proficiency as an entry requirement. This suggests that language proficiency may be associated with one’s own mental health and/or ability to access health care.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".