Parental perspectives on consent for participation in large-scale, non-biological data repositories
Bibliographic record
Abstract
BACKGROUND: Data sharing presents several challenges to the informed consent process. Unique challenges emerge when sharing pediatric or pregnancy-related data. Here, parent preferences for sharing non-biological data are examined. METHODS: Groups (n = 4 groups, 18 participants) and individual interviews (n = 19 participants) were conducted with participants from two provincial, longitudinal pregnancy cohorts (AOB and APrON). Qualitative content analysis was applied to transcripts of semi-structured interviews. RESULTS: Participants were supportive of a broad, one-time consent model or a tiered consent model. These preferences were grounded in the perceived obligations for reciprocity and accuracy. Parents want reciprocity among participants, repositories and researchers regarding respect and trust. Furthermore, parents' worry about the interrelationships between the validity of the consent processes and secondary data use. CONCLUSIONS: Though parent participants agree that their research data should be made available for secondary use, they believe their consent is still required. Given their understanding that obtaining and informed consent can be challenging in the case of secondary use, parents agreed that a broad, one-time consent model was acceptable, reducing the logistical burden while maintaining respect for their contribution. This broad model also maintained participant trust in the research and secondary use of their data. The broad, one-time model also reflected parents' perspectives surrounding child involvement in the consent process. The majority of parents felt decision made during childhood were the parents responsibility and should remain in parental purview until the child reaches the age of majority.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".