Examining unmet healthcare needs by immigration status among Canadian adults
Bibliographic record
Abstract
Immigrants have 18% lower risk of reporting unmet healthcare needs compared to non-immigrants which can be explained by the “healthy immigrant effect”, whereby the health of immigrants is better at the time of arrival and gradually deteriorates and converges with the Canadian-born population. People with high unmet healthcare needs are higher utilizers of health services. There was no information on how (and if) unmet healthcare needs predict health service utilization and its impact on life satisfaction among immigrant populations and if the relationship varies by length of stay in Canada. The study objectives were to: 1) estimate and compare reported unmet healthcare needs by immigration status. 2) estimate and compare reasons for reported unmet healthcare needs by immigration status; 3) examine relationship between unmet healthcare needs and health service utilization by immigration status. 4) examine relationship between unmet healthcare needs and life satisfaction by immigration status. 5) determine if there is a significant association between health service utilization and life satisfaction by immigration status, controlling for unmet healthcare needs. This was a secondary analysis of cross-sectional data from the Canadian Community Health Survey, cycle 2014 master data file. Individuals who were 18 years of age and older at the time of survey were included and divided into three groups based upon their years of residence in Canada: 1) recent immigrants (≤ 5 years); 2) Long-term immigrants (>5 years); and 3) Canadian-born population. Results indicated that Canadian-born population reported a significantly higher proportion of unmet healthcare needs followed by recent-immigrants and long-term immigrants. “Cost” was the most commonly reported reason for unmet healthcare need among recent immigrants and third most common among long-term immigrants and non-immigrants. Individuals with unmet healthcare needs were more likely to use physician services and reported low life satisfaction after adjusting for demographics and health-related characteristics. Individuals who used dental services were less likely to report unmet healthcare needs and low life satisfaction after adjusting for demographics and health-related characteristics. This study focuses on challenges accessing health services, especially, by immigrant population and has the potential to inform policy implications to address barriers accounting for health inequity.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".