165 WHAT FACTORS ARE ASSOCIATED WITH ADVANCED CARE PLANNING IN COMMUNITY-DWELLING OLDER PEOPLE?
Bibliographic record
Abstract
Abstract Background Advance Care Planning (ACP) involves expressing wishes regarding your future medical care and/or preferences about your end-of-life in the event of serious illness. The aim of this study was to clarify the proportion of community-dwelling older people who engage in ACP and what factors are independently associated with ACP. Methods Participants aged ≥60 years (n = 4,831, mean age 71 years) at Wave 4 of the Irish Longitudinal Study on Ageing were asked: Have you made your wishes/preferences known about the kind of care that you would like to receive in the event of serious illness? If yes, they were asked if this had been documented informally (family/carers or medical professionals) or formally (by written advanced care plan). Logistic regression models assessed the association of covariates of interest with ACP. Results One quarter of the study sample (1,153/4,831) had an ACP. Only 10% (119/1,153) had ACP documented in writing, while only 2% (27/1,153) had discussed ACP with a healthcare professional. Age ≥ 80 years (OR 1.63 (1.31–2.02)), female sex (OR 1.58 (1.37–1.83)), higher educational attainment (OR 1.42 (1.18–1.71), poorer self-rated health (OR 1.58 (1.04–2.39) and lower levels of religiosity (OR 1.50 (1.03–2.19) were independently associated with ACP. Conclusion While ACP may have benefits in extending autonomy and facilitating decision-making, only 1 in 4 of this population-representative sample of older people had engaged in ACP, with only 1 in 50 having their ACP documented in writing. Further work is therefore required to educate the public and healthcare professionals regarding the benefits of ACP.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".