A Confluence of Cultures: Advance Care Planning in Long-Term Care Settings
Bibliographic record
Abstract
Context: While policies may promote Advance Care Planning (ACP) discussions in long-term care (LTC) settings, practices often result in outcomes different from residents’ wishes. We attribute this to a confluence of cultures: healthcare; LTC settings; mainstream societal; and individuals’ ethno-cultures. This research explores these cultures as reflected in focus group discussions conducted with residents and family-of-residents in two LTC homes: one exclusively Chinese (EC); one multicultural (MC). Method: Fourteen residents and 13 family members participated in the four focus groups. Discussions were audio-recorded, transcribed, and themes were extracted and compared. Results: Four themes characterized residents’ discussions: 1-Variations in Range/Type of ACP Discussions/Actions; 2-Care of Family; 3-Reliance on Staff; and 4-Quality-of-Life at End-of-Life. Exclusively Chinese residents expressed reluctance to speak about ACP, were more likely to state “family would handle it,” less likely to call upon staff, and more acquiescent concerning death. Multicultural residents were more likely to pejoratively mention pull or absence of family and reliance upon staff; also, wanting personal awareness and control at end-of-life. Family themes were 1-Timing/Focus of ACP Discussions, 2-Communication with Family, 3-Care Home and Staff Influences, and 4-Cultural and Religious Issues. Exclusively Chinese families spoke of need to involve family in ACP discussions inclusive of residents and of Chinese cultural influences on ACP. Multicultural families reported being “taken by surprise” and feeling “overwhelmed” by requests to engage in ACP and document completion on behalf of residents. Conclusion: Findings provide evidence of multiple cultural influences on ACP in LTC but existing institutional policies and practices offer little direction and support on how to balance/prioritize them. Our analyses may provide a starting point.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| 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".