Attitudes of the General Public Toward Alternative Consent Models
Bibliographic record
Abstract
OBJECTIVE: To assess the general public's attitudes toward various consent models and data management strategies for critically ill adults eligible to participate in a low-risk randomized trial. METHODS: A self-administered survey was conducted at public locations in Toronto to elucidate the general public's attitudes toward various consent models for participation in a low-risk randomized trial when a substitute decision maker was available, unavailable, or did not exist, as well as to assess attitudes toward strategies for data management in patients enrolled under a substitute decision maker's consent who later decline further participation. RESULTS: We surveyed 221 citizens. Most respondents (64%-74%) wanted to be considered for participation. When a substitute decision maker was available, similar proportions of respondents were comfortable with the substitute decision maker providing consent, deferred consent, and their substitute decision maker being asked if the respondent would "object to participating." If a substitute existed but was unavailable, most participants were comfortable with waived consent. If a substitute did not exist, respondents expressed comfort with 4 consent models: an attending physician model, a 2-physician model (1 involved in care), deferred consent, and waived consent. Compared with any physician, respondents preferred their attending physician to be involved in decisions about their research participation, especially in the absence of a substitute decision maker. Nearly three-fourths of respondents supported data management strategies that enabled use of their primary outcome; moreover, 58% believed that data collected before their decision to decline further participation should be included. CONCLUSIONS: Most respondents were interested in participating in a low-risk trial. Respondents endorsed a variety of approaches to obtaining consent in the presence or absence of substitute decision makers and many would be comfortable if their data were used despite a decision to decline further participation.
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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 | 0.029 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".