Academic family physicians' perception of genetic testing and integration into practice: a CERA study.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Genetic testing for a variety of diseases is becoming more available to primary care physicians, but it is unclear how useful physicians perceive these tests to be. We examined academic family physicians' perception of and experiences with clinical genetic testing and direct-to-consumer genetic testing. METHODS: This study is an analysis of a survey conducted as part of the Council of Academic Family Medicine Educational Research Alliance (CERA). Academic family physicians in the United States and Canada were queried about their perception of genetic testing's utility, how frequently patients ask about genetic testing, and the importance of genetic testing in future practice and education of students and residents. RESULTS: The overall survey had a response rate of 45.1% (1,404/3,112). A majority (54.4%) of respondents felt that they were not knowledgeable about available genetic tests. Respondents perceived greater utility of genetic tests for breast cancer (94.9%) and hemochromatosis (74.9%) than for Alzheimer's disease (30.3%), heart disease (25.4%), or diabetes (25.2%). Individuals with greater self-perceived knowledge of genetic tests were more likely to feel that genetic testing would have a significant impact on their future practice (23.1%) than those with less knowledge (13.4%). Respondents had little exposure to direct-to-consumer genetic tests, but a majority felt that they were more likely to cause harm than benefit. CONCLUSIONS: Academic family physicians acknowledge their lack of knowledge about genetic tests. Educational initiatives may be useful in helping them incorporate genetic testing into practice and in teaching these skills to medical students and residents.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".