Understanding advance care planning within the South Asian community
Bibliographic record
Abstract
BACKGROUND: Advance care planning (ACP) is a process of reflection on and communication of a person's future health-care preferences. Evidence suggests visible minorities engage less in ACP. The South Asian ethnic group is the largest visible minority group in Canada, and information is needed to understand how ACP is perceived and how best to approach ACP within this diverse community. OBJECTIVE: To explore perspectives of South Asian community members towards ACP. DESIGN: Peer-to-peer inquiry. South Asian community members who graduated from the Patient and Community Engagement Research programme (PaCER) at the University of Calgary utilized the PaCER method (SET, COLLECT and REFLECT) to conduct a focus group, family interviews and a community forum. SETTING AND PARTICIPANTS: Fifty-seven community-dwelling men and women (22-86 years) who self-identified with the South Asian community in Calgary, Alberta, Canada. RESULTS: The concept of ACP was mostly foreign to this community and was often associated with other end-of-life issues such as organ donation and estate planning. Cultural aspects (e.g. trust in shared family decision making and taboos related to discussing death), religious beliefs (e.g. fatalism) and immigration challenges (e.g. essential priorities) emerged as barriers to participation in ACP. However, participants were eager to learn about ACP and recommended several engagement strategies (e.g. disseminate information through religious institutions and community centres, include families in ACP discussions, encourage family physicians to initiate discussions and translate materials). CONCLUSIONS: Use of a patient engagement research model proved highly successful in understanding South Asian community members' participation in ACP.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".