Does the public feel prepared to be substitute decision-makers? Gaps in preparedness and support for a high school curriculum: a national survey
Bibliographic record
Abstract
BACKGROUND: Substitute decision-makers (SDMs) make decisions on behalf of patients who do not have capacity, in line with previously expressed wishes, values and beliefs. However, miscommunications and poor awareness of previous wishes often lead to inappropriate care. Increasing public preparedness to communicate on behalf of loved ones may improve care in patients requiring an SDM. METHODS: We conducted an online survey in January 2019 with a representative sample of the Canadian population. The primary outcome was self-reported preparedness to be an SDM. The secondary outcome was support for a high school curriculum on the role of SDMs. The effect of socio-demographics, known enablers and barriers to acting as an SDM, and attitudes towards a high school curriculum were assessed using multivariate analysis. RESULTS: Of 1,000 participants, 53.1% felt prepared to be an SDM, and 75.4% stated they understood their loved one's values. However, only 55.6% reported having had a meaningful conversation with their loved one about values and wishes, and only 61.7% reported understanding the SDM role. Engagement in advance care planning for oneself was low (23.1%). Age, experience, training and comfort with communication were associated with preparedness in our multivariate analysis. A high school curriculum was supported by 61.1% of respondents, with 28.3% neutral and 10.6% against it. INTERPRETATION: There is a gap between perceived and actual preparedness to be an SDM. Many report understanding their loved one's values yet have not asked them about wishes in illness or end of life. The majority of respondents support high school education to improve preparedness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".