Citizens, research ethics committee members and researchers’ attitude toward information and consent for the secondary use of health data: Implications for research within learning health systems
Bibliographic record
Abstract
A provincial survey was conducted to assess citizens, research ethics committee (REC) members and researchers’ attitude toward information and consent for the secondary use of health data for research within a learning health system (LHS). The results show that: 1) reuse of health data for research to advance knowledge and improve care is values by all parties; 2) consent regarding health data use for research is fundamental particularly to citizens, even when the data are de-identified; 3) a secure website to support the information and consent processes was appreciated by all. Researchers were more comfortable with the use of de-identified health data for research without informing nor seeking consent from people than citizen and REC member respondents. This survey was part of a larger project that aims at exploring public perspectives on alternate approaches to the current consent models in Quebec to take into consideration the unique features of LHS. The revised consent model will need to ensure that citizens are given the opportunity to be better informed about incoming researches with their health data and have their say, when possible, in the use of their data.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.257 | 0.223 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.026 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".