Utilization of healthcare by immigrants in Canada: a cross-sectional analysis of the Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Immigrants to Canada face unique barriers to health care, which leads to inequities in health care utilization. Lower utilization of health care by immigrants to Canada is associated with the deteriorating health of individual immigrants as well as increased costs to the health care system. The existing literature suggests that time since immigration is an important predictor for utilization of health care for Canadian immigrants; however, few studies have included this variable in their analysis. This study aims to examine the relationships between having a regular health care provider and time since immigration, and number of medical consultations in the past year and time since immigration. METHODS: A secondary cross-sectional data analysis using Andersen and Newman's Framework of Health Service Utilization and data from the 2015-2016 Canadian Community Health Survey (CCHS) was conducted to examine health care utilization among immigrants in Canada. We used multiple logistic regression to examine the relationship between time since immigration and having a regular physician and negative binomial regression to compare the number of consultations of recent (less than 10 years since immigration) and established (10 or more years since immigration) immigrants. RESULTS: Eighty four percent of immigrant respondents to CCHS 2015-2016 had a regular health care provider. After controlling for other independent variables, established immigrants were 1.75 (95% confidence interval: 1.45-2.10) times more likely to have a regular health care provider compared to recent immigrants. Immigrants had a mean of 3.37 (standard deviation 4.53) medical consultations in the preceding year. There was no difference in the mean number of medical consultations by recent and established immigrants. CONCLUSIONS: After controlling for other independent variables, this study found that time since immigration had a significant effect on having a regular provider but not on number of consultations. Differences in health care utilization for recent and for established immigrants observed in this study may be partially explained by Canada's evolving immigration policy and the economic and social integration of immigrants over time.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".