Prevailing Public Perceptions of the Ethics of Gene Therapy
Bibliographic record
Abstract
Gene therapy research is advancing rapidly, and hopes of treating a large number of brain disorders exist alongside ethical concerns. Most surveys of public attitudes toward these ethical issues are already dated and the content of these surveys has been researcher-driven. To examine current public perceptions, we developed an online instrument that is responsive and relevant to the latest research about ethics, gene therapy, and the brain. The 16-question survey was launched with the platform Amazon Mechanical Turk and was made available to residents of Canada and the United States. The survey was divided into six themes: (1) demographic information, (2) general opinions about gene therapy, (3) medical applications of gene therapy, (4) identity and moral/belief systems, (5) enhancement, and (6) risks. We received and analyzed responses from a total of 467 participants. Our results show that a majority of respondents (>90%) accept gene therapy as a treatment for severe illnesses such as Alzheimer disease, but this receptivity decreases for conditions perceived as less severe such as attention deficit hyperactivity disorder (79%), and for nontherapeutic applications (47%). The greatest area of concern for the application of gene therapy to brain conditions is the fear of not receiving sufficient information before undergoing the treatment. The main ethical concerns with enhancement were the potential for disparities in resource allocation, access to the procedure, and discrimination. When comparing these data with those from the 1990s, our findings suggest that the acceptability of gene therapy is increasing and that this trend is occurring despite lingering concerns over ethical issues. Providing the public and patients with up-to-date information and opportunities to engage in the discourse about areas of research in gene therapy is a priority.
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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 | 0.015 | 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".