Attitude and Knowledge about Genetics and Genetic Testing
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.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".