Planning for End-of-Life Care: Findings from the Canadian Study of Health and Aging
Bibliographic record
Abstract
Steps involved in formalizing end-of-life care preferences and factors related to these steps are unclear in the literature. Using data from the third wave of the Canadian Study of Health and Aging (CSHA-3), we examined the relations between demographic and health predictors, on the one hand, and three outcomes, on the other (whether participants had thought about, discussed , or formalized their end-of-life preferences), and considered, as well, whether relations existed among the three outcomes. Canadian region of residence, female gender, and more years of education predicted having thought about preferences; region of residence, female gender, and lack of cognitive impairment predicted discussion of preferences; and region of residence and not being married predicted whether formal documents were in place. Ontario residents were most likely to have thought about, discussed, and formalized their preferences, whereas Atlantic residents were least likely to. Finally, having thought about preferences was associated with discussion, and having thought about and having discussed preferences were each associated with formalization of preferences. These findings are in keeping with the position that Advance Directives (AD) execution is a multi-stage process. A better understanding of this process may prove useful for the development of interventions to promote planning for end-of-life care.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".