Attitude and Knowledge about Genetics and Genetic Testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Increasing numbers of health care users may be confronted with new genetic knowledge and discoveries that offer new types of medical decision-making. How people use these new insights and make decisions about genetic risk depends, at least in part, on their knowledge and attitudes about human genetics. METHODS: A postal survey administered to 560 women who had been offered prenatal screening in Ontario measured knowledge about, and attitudes toward, genetic testing and the uses of genetic information. RESULTS: Respondents strongly supported the use of genetic information to improve disease diagnosis and to help understand disease causes; however, people also held a more critical attitude towards certain aspects of testing and genetic information. Relatively high levels of knowledge about genetics were also observed in this sample, although there were deficits in specific areas (e.g., transmission patterns). CONCLUSIONS: Despite overall positive attitudes towards genetics, participants held more critical attitudes towards certain aspects of testing and the uses of genetic information. It would be unwise for genetics policy-makers and stakeholders to assume that a better-informed public would automatically be more supportive of all genetics research and new genetic discoveries.
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.
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 | 0.000 | 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 it