Knowledge of the legislation governing proxy consent to treatment and research
Bibliographic record
Abstract
OBJECTIVE: To assess the knowledge of four groups of individuals regarding who is legally authorised to consent to health care or research involving older patients. DESIGN: A provincewide postal survey. SETTING: Province of Quebec, Canada. PARTICIPANTS: Three hundred older adults, 434 informal caregivers of cognitively impaired individuals, 98 researchers in aging and 136 members of research ethics boards (REBs). MEASUREMENTS: Knowledge was assessed through a pretested postal questionnaire comprising five vignettes that describe hypothetical situations involving an older adult who requires medical care or is solicited for research. The respondent had to identify the person who is legally authorised to provide consent. RESULTS: Nearly 80% of all respondents provided the correct answer when the hypothetical scenario depicted a person who was competent to consent or incompetent but legally represented. Knowledge was worse (from 2% among older adults to 44% among REB members) for the scenario describing a research situation that involved an incompetent adult without a legal guardian. CONCLUSION: The observed lack of knowledge raises doubts about the ability of current legislation to truly protect the rights of older adults with diminished decision making capacity. It points to the need for educational programmes aimed at increasing public awareness of the legislation put in place for those requiring special protection.
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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 | 0.033 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".