Three Essays on Health and Health Behaviours of Immigrants
Bibliographic record
Abstract
This thesis focuses on the comparison between immigrants and non-immigrants with respect to various health-related behaviours perspective/viewpoints. Specifically, this thesis comprises three essays. First, I investigate any differences in the factors for utilizing general practitioners (GP) and specialists (SP) between immigrants and non-immigrants in Canada. Second, I examine the causal effects of language proficiency on the health and health behaviours of immigrants to Canada. Finally, I investigate whether there are any differences in the claiming patterns of the Medical Expense Tax Credit (METC) and/or Medical Expense Supplement (MES) for immigrants compared to non-immigrants in Canada. Chapter 1 investigates any differences in healthcare utilization patterns between immigrants and non-immigrants. We implement a two-part model, where the first part applies logistic regressions to assess factors associated with visiting a physician, and the second applies zero-truncated negative binomial regression models to capture the frequency of using healthcare services, conditional on having at least one visit. Our results show that the patterns of healthcare utilization are different for immigrants compared to non-immigrants; differences are also observed by gender and age. More specifically, prescription drug insurance coverage and chronic conditions play opposing roles for male and female immigrants compared to their non-immigrants counterparts. Moreover, the number of years since migration is an important factor in increasing the probability of any general practitioner (GP) and specialist (SP) visit for all immigrants. Chapter 2 is to my knowledge, the first research on the causal effects of language proficiency on health outcomes and healthcare utilization of immigrants in Canada. My finding contradicts the idea that immigrants with poor language facilities are less likely to have a regular doctor. I find that good self-reported health is positively associated with language proficiency. However, I find no statistically significant causal effect of language proficiency on reporting ‘good mental health’. In addition, I find strong evidence that the utilization of hospital and mental health care services are positively associated with being English-language proficient even after controlling for many possible sets of factors. Chapter 3 contributes by supporting existing literature, but with a completely different dimension: the medical tax perspective. I am unaware of any previous research that directly compares the claim patterns of the Medical Expense Tax Credit (METC) and/or refundable Medical Expense Supplement (MES) for immigrants with those of non-immigrants in Canada. My results show that there are differences in the proportions of tax filers who claimed the METC and/or MES, and the amounts of a claim for the METC and/or MES for immigrants compared to non-immigrants; differences are also observed by age, years since migration (YSM), province and immigration categories. In both couples and single families, a lower proportion of immigrant tax filers claimed gross immediate family medical expenses (GME), potential METC claims, and METC refunds compared to non-immigrants. In the case of single families, a higher proportion of non-immigrant tax filers claimed MES compared to their immigrant counterparts.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".