Advance care planning in British Columbia: awareness and engagement (2012–2020)
Bibliographic record
Abstract
OBJECTIVES: This study examines trends in advance care planning (ACP) awareness, engagement, attitude and experiences among the public in British Columbia (BC), Canada, from 2012 to 2020. This time period is of relevance as it includes provincial education initiatives. We also explored demographic characteristics associated with lower ACP awareness and engagement, to inform future initiatives. METHODS: Longitudinal data from two provincial polls in 2016 and 2020, along with BC-specific data extracted from a national poll in 2012, were analysed to investigate ACP awareness, engagement and attitudes. Demographic characteristics associated with ACP were examined using binomial and ordinal logistic regression. RESULTS: We observed an increase in awareness of ACP between 2012 and 2020 (from 15.2% to 32.9%). A significant rise in engagement was also observed, with more respondents reporting ACP conversations with healthcare providers between 2016 and 2020 (8.8%-14%) and documenting their healthcare wishes since 2012 (11.6%-28.0%). Demographic analysis revealed that older, female and more educated individuals were more likely to participate in ACP activities. CONCLUSIONS: This study highlights positive trends in ACP awareness and engagement in BC over the study period. Despite lower rates in some variables, the observed increases suggest increasing awareness over time, with potential for further improvement. However, disparities persist among different demographic groups, highlighting the need for targeted efforts to improve ACP knowledge and participation, especially among younger adults, males, single individuals and those with lower education levels.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".