Development and psychometric evaluation of a new genetic attitudes questionnaire in two national samples of United States residents
Bibliographic record
Abstract
With the proliferation of direct-to-consumer genetic testing and the advent of personalized medicine, protecting genomic data and defining acceptable use cases is a pressing policy issue. Existing measures of public attitudes toward genomic data and how they should be handled rely primarily on single-item measures or ad hoc scales with unknown psychometric properties. In this paper, we rely on two surveys to develop a psychometrically sound genetic utility, inaccuracy, and privacy (GUIP) questionnaire, compare this measure to a single-item measure, examine how this measure varies across demographic and attitudinal factors, and explore its predictive power for genetic data sharing. The first survey was exploratory and included both open- and closed-ended questions to gauge genetic attitudes. We oversampled respondents with minoritized racial and ethnic identities (Black, Latinx, and Indigenous) to compare how identity relates to attitudes about genetics. The second survey facilitated a factor analysis on items developed from the open-ended responses, examined measurement invariance in the factor structure across racial and ethnic identities, and explored how the GUIP varies across demographic and attitudinal factors. Finally, we examined the GUIP as a predictor of genetic data sharing with physicians, medical researchers, pharmaceutical companies, and law enforcement and willingness to participate in genetic research.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".