Biobanking for Genomic and Personalized Health Research: Participant Perceptions and Preferences
Bibliographic record
Abstract
Introduction: Biospecimens and associated data are invaluable tools in Genomics and Personalized Health (GAPH) research and can aid in the discovery of disease etiology and the development of therapeutics. Objective: To examine the experiences of patients invited to a particular GAPH study, Spectrometry in TIA Rapid Assessment (SpecTRA), and to explore broader biospecimen and data sharing preferences among a larger group of patients who had opted into a Permission to Contact for research program. Methods: An electronic survey was e-mailed to 515 participants. The survey was completed by 38% of participants, an unspecified number of whom were also SpecTRA participants. Results: Of those respondents who recalled participating in SpecTRA, 96% strongly agreed, agreed, or were neutral when asked if they received enough information to make an informed decision. Seventy-two percent agreed and 20% were neutral when asked if their study questions were addressed. Ninety-six percent of all respondents felt that SpecTRA's aim to develop a proteomic test for stroke was a worthwhile investment for health care, 98% said they were willing to provide a sample and/or information to facilitate the project's goals, and 96% to health research in general. Fifty-three percent of all participants suggested they would be comfortable sharing health information collected during SpecTRA with for-profit organizations, 87% with nonprofit organizations, and 38% said it matters to them where in the world their sample/information would be sent. Conclusions: Our results suggest that while there is room for improvement in providing adequate information to enable participants' understanding of the purpose of GAPH studies such as SpecTRA, patients are supportive of GAPH in general. Results also suggest that willingness to participate would likely be impacted by factors such as the study's commercial and national affiliations. This study indicates that further work is required to guide improvements on how the GAPH research community describes studies to potential participants, and to enable participation options that incorporate variable participant preferences.
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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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".