Proxy medical decision-making: national survey
Bibliographic record
Abstract
OBJECTIVES: Population ageing and increased care needs lead to adults making consequential medical decisions for others, potentially impacting treatment and end of life. We aim to describe the prevalence of medical decision-making by proxy among the national population and associated demographic and care factors. METHODS: We designed a cross-sectional online survey with a nationally representative adult cohort with an 80% participation rate. 311 Singapore residents completed the survey. RESULTS: 73% of respondents reported having ever assisted others with medical decisions, while 58% have ever assisted with activities of daily living (ADLs), and 88% with instrumental ADLs (IADLs). Having a digital caregiver account, having a lasting power of attorney as a donee and assisting with ADLs and IADLs are significantly associated with proxy medical decision-making. Gender, ethnicity, income and age did not appear to have a significant impact. CONCLUSIONS: A majority of Singapore adults assist others with caregiving tasks and medical decision-making. These helping behaviours are often performed informally, which may increase decisional burden for caregivers and potential abuse of power.
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
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".