Primary Care Physicians’ and Hospitalists’ Experience with Advance Care Planning with South Asian Canadian Older Adults before and during COVID-19
Bibliographic record
Abstract
Few older adults discuss their end-of-life care wishes with their physician, and even fewer minorities do this. We explored physicians' experience with advance care planning (ACP) including the barriers/facilitating factors encountered when initiating/conducting ACP discussions with South Asians (SA), one of Canada's largest minorities. Eleven primary care physicians (PC) and 11 hospitalists with ≥ 15 per cent SA patients ≥ 55 years of age were interviewed: 10 in 2020, 12 in 2021. Thematic analysis of transcripts indicated that cultural and communication barriers, physician's specialization, SA older adults' lack of ACP awareness, and decision-making deference to family and physicians were barriers to ACP discussions. Although the COVID-19 pandemic impacted physicians' practices, contrary to our hypothesis most reported no change in frequency of ACP discussions. Although ACP discussions were viewed as best conducted by PC physicians, only 55 per cent had ACP training and only 64 per cent had used ACP tools. Training in ACP facilitation, concerning ACP tool usage, and training in patient-physician communication are recommended.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".