Advance Directives for Health Care and Research
Bibliographic record
Abstract
Patients suffering from Alzheimer disease and other types of dementia gradually lose their decision-making capacity. Advance directives have been widely promoted as a means to maintain some control over one's life in the event of decisional incompetence. This study used data from a recent postal survey conducted in Quebec, Canada to: 1) estimate the prevalence of formal and informal advance directives for health care and research among community-dwelling older adults presumed free of cognitive deficits; and 2) characterize those who have communicated their preferences regarding health care and research participation. Prevalence rates vary from 7.4% (formal advance directives for research) to 42.3% (informal advance directives for health care). Following multivariate logistic regressions, individuals who have communicated their wishes regarding future health care were found to be older, predominantly women, and to more often know someone with cognitive impairment. Those who have expressed their wishes regarding future research involvement were more inclined to participate in research. They were also more likely to have discussed or written advance directives for health care. The finding that only a small proportion of older adults have discussed future research participation with their families points to the need to find effective ways to promote advance directives for research in this population.
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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".