A Comparison of Chinese and Korean Older Adult Immigrants’ Transnational Healthcare Practices in Toronto, Canada: A Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: While immigrants represent 21% of Canada's total population, they represent 30% of the country's older population. Sociocultural and economic barriers to the Canadian healthcare system have been frequently reported among older adult immigrants. These barriers are intricately linked to a vastly understudied phenomenon-transnational health practices (THP), which may involve travelling to home countries for healthcare, accessing medicine and health-related information and resources linked to home countries. This study aimed to explore the relationships among local healthcare experiences in Canada, individual characteristics and use of THP among older adult immigrants. METHODS: A mixed-methods approach was used combining statistical, spatial and qualitative methods to analyze group patterns of THP and its influencing factors. Primary data was collected through surveys and focus groups of older Mainland Chinese and older South Korean immigrants residing in Toronto. They are the two largest East Asian groups in Canada, with documented transnational ties with their home country. RESULTS: The study found that THP were sought by both groups but were more prevalent among older Chinese immigrants. By integrating quantitative and qualitative analyses, the study revealed complex relationships between THP and barriers in local healthcare access relating to wait times, cost, language, availability, spatial accessibility and quality of care, for different types of care including primary, specialist, eye and dental care. CONCLUSIONS: The study generates new knowledge on THP in Canada and adds to the growing body of literature on transnational healthcare practices and behaviours among migrants across different countries and regions. It provides implications to inform health policy and deliver care for older adult immigrants as their populations continue to increase.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".