Do Immigrants Use Less Health Care than Non-immigrants? A Population-based Study among People living with Multimorbidity in Canada
Bibliographic record
Abstract
Abstract Background Immigrants face unique health care barriers, which can negatively impact their health service use and overall health. Those with multimorbidity may face a particular challenge given its association with increased need for health care. The purpose of this study was to compare health care utilization, as measured by the number of visits to family physicians and specialists, between immigrants and Canadian-born individuals living with multimorbidity. Methods A cross-sectional analysis was carried out using data from the 2015-2016 cycles of Canadian Community Health Survey (CCHS) on 9,014 study participants living with multimorbidity. The study utilized Andersen and Newman's behavioral model as a conceptual framework to identify quantifiable predictors associated with health service utilization. For the entire sample as well as for male and female subsamples, statistical models were fitted using negative binomial regressions to account for the count nature of the outcome variables. Results After adjusting for relevant confounders, no statistically significant differences were observed between immigrants and Canadian-born respondents in the number of visits to family physicians or specialists. However, subgroup analysis revealed that female immigrants with multimorbidity had considerably fewer visits to family physicians than Canadian-born females (Incident Rate Ratio [IRR]=0.86, 95% CI: 0.76-0.98), while for males these differences were not significant (IRR=1.03, 95% CI: 0.87-1.21). Conclusions Future research should focus on longitudinal studies to track the health status of immigrants over time, particularly those living with multimorbidity. Moreover, public health policies should be implemented to reduce cultural and social barriers to health care, with a special focus on female 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".