What do Canadians think of advanced care planning? Findings from an online opinion poll
Bibliographic record
Abstract
BACKGROUND: Advance care planning (ACP) has the potential to increase patient-centred care, reduce caregiver burden, and reduce healthcare costs at the end of life. Current levels of public participation in ACP activities are unknown. The purpose of this study was to determine the level of engagement of average Canadians in ACP activities. METHODS: Data come from an on-line opinion poll of a national sample of respondents who were asked five questions on ACP activities along with their sociodemographic characteristics. RESULTS: Respondents were from all provinces of Canada, 52% were women, and 33% were between 45 years and 54 years of age. Of 1021 national sample respondents, 16% were aware of the term, ACP (95% CI 13% to 18%), 52% had discussions with their family or friends (95% CI 49% to 55%), and 10% had discussions with healthcare providers (95% CI 8% to 12%). Overall, 20% (95% CI 18% to 22%) of respondents had a written ACP and 47% (95% CI 44% to 50%) had designated a substitute decision maker. Being older was associated with significantly more engagement in ACP activities and there were significant differences in ACP engagement across Canada. CONCLUSIONS: Although only a small proportion of Canadians are aware of the formal term, ACP, a higher percentage of Canadians are actually engaged in ACP, through either having discussions or making decisions about end-of-life care. Older citizens are more likely to be engaged in ACP and there are geographic differences in the level of ACP engagement across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".