Community engagement with genetics: public perceptions and expectations about genetics research
Bibliographic record
Abstract
BACKGROUND: Knowledge of molecular biology and genomics continues to expand rapidly, promising numerous opportunities for improving health. However, a key aspect of the success of genomic medicine is related to public understanding and acceptance. DESIGN: Using community consultations and an online survey, we explored public attitudes and expectations about genomics research. RESULTS: Thirty-three members of the general public in Newfoundland, Canada, took part in the community sessions, while 1024 Atlantic Canadians completed the online survey. Overall, many participants noted they lacked knowledge about genetics and associated research and took the opportunity to ask numerous questions throughout sessions. Participants were largely hopeful about genomics research in its capacity to improve health, not only for current residents, but also for future generations. However, they did not accept such research uncritically, and a variety of complex issues and questions arose during the community consultations and were reflected in survey responses. DISCUSSION: With the proliferation of biobanks and the rapid pace of discoveries in genomics research, public support will be crucial to realize health improvements. If researchers can engage the public in regular, transparent dialogue, this two-way communication could allow greater understanding of the research process and the design of efficient and effective genetic health services, informed by the public that will use them.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".