Essays in immigrant health and health service utilization in Canada
Bibliographic record
Abstract
This dissertation attempts to make important contributions to the small but growing literature on immigrant health and health service utilization. As the main data source, it makes use of the first three cycles of the Canadian National Population Health Survey. The first essay, "Understanding the Healthy Immigrant Effect in Canada" examines the dynamics of immigrants' health and health service utilization behaviour with tenure in Canada. The panel nature of the data is exploited to distinguish between changes in immigrant health and utilization behaviour over time, and changes in the types of immigrants that have entered the country over time. This distinction cannot be made using cross-sectional analysis as is common in the existing literature. The findings suggest that the decline in health following immigration found in cross section data remains when controls for cohort of entry and survey year are included. As well, the dynamics of immigrant health service utilization is consistent with a story of initial access barriers to newly arrived immigrants followed by assimilation to the Canadian norm for services that are more preventative in nature. The second essay, "Social Networks and Health Service Utilization in Canada" explores whether an individual's decision to use various health services is affected by those in their social network. Regional and language group variation in the data is exploited to construct a variable that captures both quantity and quality characteristics of networks. Strong and robust evidence of network effects is found on the decision to utilize preventative services. These are the same services for which access barriers were found in the first essay. As well, this work presents novel evidence that utilization of health services by immigrants increases in the number of doctors that speak their language in their neighborhood.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".