“And who would question God?”: Patient engagement and healthcare decision-making of South-Asian older adults in the Canadian healthcare system
Bibliographic record
Abstract
Abstract With the increase in ethnocultural diversity in the Canadian demographic landscape, it is imperative for healthcare providers and policy makers to understand the needs and preferences of racialized immigrant older adults. Previous research has shown that to increase patient satisfaction with care and lower treatment costs it is important to effectively involve patients in their care. However, we currently lack the understanding of how racialized immigrant older adults want to engage as patients, the factors that influence their involvement in their own care, and who they want involved in the decisions surrounding their care. To address this gap in the literature specifically for the South Asian community, one of Canada’s largest and fastest growing populations, our study aimed to understand South Asian older adults’ experiences with and approaches to patient engagement and shared decision-making. We conducted in-depth individual and dyadic interviews (n=28) in six languages, utilizing a multilingual cross-cultural qualitative approach. Our findings highlight the nuances of language and how miscommunication can arise even when patients and providers are conversing in the same language. Our study also found that patient engagement and shared decision-making, including the desire for family involvement, is heavily influenced by both culture and gender. Additionally, perceptions of patients regarding the status of physicians can have a notable influence on patient engagement, leading to an increased tendency for patients to agree with the physicians’ approach to care. These findings suggest that effective engagement between providers and patients require a tailored approach that extends beyond white-centric approaches to decision-making and communication.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".