Identifying Aspects of Public Attitudes Toward Whole Genome Sequencing to Inform the Integration of Genomics into Care
Bibliographic record
Abstract
INTRODUCTION: The growth of global sequencing initiatives and commercial genomic test offerings suggests the public will increasingly be confronted with decisions about sequencing. Understanding public attitudes can assist efforts to integrate sequencing into care and inform the development of public education and outreach strategies. METHODS: A 48-item online survey was advertised on Facebook in Eastern Canada and hosted on SurveyMonkey in late 2018. The survey measured public interest in whole genome sequencing and attitudes toward various aspects of sequencing using vignettes, scaled, and open-ended items. RESULTS: While interest in sequencing was high, critical attitudes were observed. In particular, items measuring features of patient control and choice regarding genomic data were strongly endorsed by respondents. Majority wanted to specify upfront how their data could be used, retain the ability to withdraw their sample at a later date, sign a written consent form, and speak to a genetic counselor prior to sequencing. Concerns about privacy and unauthorized access to data were frequently observed. Education level was the sociodemographic variable most often related to attitude statements such that those with higher levels of education generally displayed more critical attitudes. CONCLUSIONS: Attitudes identified here could be used to inform the development of implementation strategies for genomic medicine. Findings suggest health systems must address patient concerns about privacy, consent practices, and the strong desire to control what happens to their genomic data through public outreach and education. Specific oversight procedures and policies that are clearly communicated to the public will be required.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".