EP01.002 Advance care planning discussion and documentation among the Dutch general public over the past 5 years: a mixed methods study
Bibliographic record
Abstract
Background Advance care planning (ACP) is increasingly advocated to perform patient-centered end-of-life care. We investigated whether the extent to which people in the Netherlands discuss or document their end-of-life preferences has changed over the past 5 years. Methods A mixed methods study among a random sample of 1456 Dutch citizens between June and October 2022 (N=1097, response 75%). A cross-sectional survey included statements on whether participants had thought about a number of issues relating to the end of life, had ever talked with a doctor about these issues, and had documented their wishes regarding medical treatments at the end of life. The statements were similar to those used in a survey that was performed in 2016. In subsequent qualitative interviews (N=15) we explored the experiences of citizens with ACP. Descriptive statistics were used to analyze questionnaire data and thematic analysis for the interviews. Results In 2022, 66% of the respondents stated that they had ever thought about issues relating to the end of their life, such as who should decide about their medical treatment when they can no longer do so themselves. In 2016 this percentage was 70%. The percentage of respondents who had spoken to a doctor about these topics was lower (13% in 2022, 12% in 2016), and only 17% (in 2022) of the respondents stated that they had documented their wishes and expectations regarding medical treatments they (did not) want to receive at the end of their lives. Interviewed citizens gave a number of reasons for not thinking or talking about those issues. for example: they had not yet have to deal with illness or it should be left to fate. Conclusion Despite increased attention for ACP, the proportion of Dutch citizens who have discussed ACP with their doctor or documented their wishes remains limited.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".