Additional file 1 of Meta-consent for the secondary use of health data within a learning health system: a qualitative study of the public’s perspective
Bibliographic record
Abstract
Additional file 1. Additional methodology information. The Additional file 1 presents details on the composition of the eight focus groups (Additional Table S1.1), a description of the meta-consent model as we defined it in our study (Additional Tables S1.2, S1.3 and S1.4) as well as the tools used with our focus group participants (Additional Tables S1.5 and S1.6). Additional Table S1.1. Focus groups’ composition. Additional Table S1.2. Meta-consent model: Set of data content characteristics {X}. Additional Table S1.3. Meta-consent model: Set of contextual characteristics {Y}. Additional Table S1.4. Meta-consent model: Set of consent choices {Z}. Additional Table S1.5. Characteristics defining categories of research projects in our meta-consent model as presented to the focus group participants. Additional Table S1.6. Example of predefining categories of research projects for which focus group participants had to express their meta-consent preferences.
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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 | 0.050 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.803 | 0.075 |
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".