Alternatives to Project-specific Consent for Access to Personal Information for Health Research: What Is the Opinion of the Canadian Public?
Bibliographic record
Abstract
OBJECTIVES: This study sought to determine public opinion on alternatives to project-specific consent for use of their personal information for health research. DESIGN: The authors conducted a fixed-response random-digit dialed telephone survey of 1,230 adults across Canada. MEASUREMENTS: We measured attitudes toward privacy and health research; trust in different institutions to keep information confidential; and consent choice for research use of one's own health information involving medical record review, automated abstraction of information from the electronic medical record, and linking education or income with health data. RESULTS: Support was strong for both health research and privacy protection. Studying communicable diseases and quality of health care had greatest support (85% to 89%). Trust was highest for data institutes, university researchers, hospitals, and disease foundations (78% to 80%). Four percent of respondents thought information from their paper medical record should not be used at all for research, 32% thought permission should be obtained for each use, 29% supported broad consent, 24% supported notification and opt out, and 11% felt no need for notification or consent. Opinions were more polarized for automated abstraction of data from the electronic medical record. Respondents were more willing to link education with health data than income. CONCLUSIONS: Most of the public supported alternatives to study-specific consent, but few supported use without any notification or consent. Consent choices for research use of one's health information should be documented in the medical record. The challenge remains how best to elicit those choices and ensure that they are up-to-date.
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 | 0.090 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".