Examining the health and healthcare experiences of older Vietnamese Canadians who have chronic conditions
Bibliographic record
Abstract
Older immigrants in Canada have poorer health and projected health outcomes as well as a harder time seeking and accessing healthcare compared to their younger, non-immigrant counterparts. Although previous research has explored the barriers that older immigrants face, the experiences of older Vietnamese immigrants have not been thoroughly examined. Older Vietnamese immigrants have been studied in other contexts, like the United States and Australia, where researchers found evidence of multifaceted barriers to health, including limited English proficiency, low health literacy, and social/cultural influences on health behaviours. Building on this research, the purpose of my study was to explore the health and healthcare experiences of older Vietnamese immigrants in Canada, who have at least one chronic condition. Intersectionality theory was also used to further understand how health is influenced by various systems of oppression. I conducted semi-structured interviews with eight older Vietnamese immigrants, aged 53-70 (average age 62). Six participants were interviewed twice, and two participants were interviewed once. The data were audio-coded and thematically analyzed. Three themes were identified. The first theme, “Finding a doctor who cares about you is hard”, compared the participants’ lack of adequate care in Vietnam with their experiences of receiving quality care for the first-time post immigration. The second theme, “You just have to try your best”, encompassed the participants’ difficulties with managing their health and accessing care as well as their complex feelings of gratitude and indebtedness to Canada. The last theme, “At this age I can’t be strong and healthy anymore”, identified the ways that participants’ health experiences and perceptions had changed as they aged in Canada, especially as they reflected on the ways that war and poverty had influenced their health decisions. These findings are discussed in relation to past research and recommendations for future research, healthcare policy, and healthcare practice are also provided. Through a deep exploration of the health and healthcare experiences of older Vietnamese immigrants in Canada, this study has further unraveled the complex ways that history, identity, and immigration influence health while also highlighting the importance of incorporating marginalized populations within healthcare research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".