What are patient perspectives on privacy and trust in digital genomic tools? A qualitative study
Bibliographic record
Abstract
Digital tools have emerged as a promising solution to increase the efficiency and capacity of genomic services. However, accessing information through internet-based applications raises concerns about privacy and security risks. As patient-facing digital tools are developed for genomic medicine, it is vital to understand and incorporate patients' perspectives on digital privacy and security. A qualitative study was conducted using semi-structured interviews and interpretive description. Thirty participants who previously received genetic testing for themselves (n = 17) or their child (n = 13) were interviewed (n = 20 females, n = 15 above 50 years old). Participants were willing to store and access genomics personal health information (PHI) in a patient-facing digital platform. The main benefit identified by participants was the ability to access and control their own PHI. Participants expressed that the benefits of digital genomics services, such as patient empowerment and personalized care, outweighed the perceived risks, such as potential data leaks. In order to minimize risks, participants emphasized the importance of transparency about the security measures in place and who would have access to their PHI. These findings inform the design of digital genomic platforms to enhance patients' sense of security, which is critical for the uptake and usage of any platform.
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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 | 0.032 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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, 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".