Primary care providers' experiences with and perceptions of personalized genomic medicine.
Bibliographic record
Abstract
OBJECTIVE: To assess primary care providers' (PCPs') experiences with, perceptions of, and desired role in personalized medicine, with a focus on cancer. DESIGN: Qualitative study involving focus groups. SETTING: Urban and rural interprofessional primary care team practices in Alberta and Ontario. PARTICIPANTS: Fifty-one PCPs. METHODS: Semistructured focus groups were conducted and audiorecorded. Recordings were transcribed and analyzed using techniques informed by grounded theory including coding, interpretations of patterns in the data, and constant comparison. MAIN FINDINGS: Five focus groups with the 51 participants were conducted; 2 took place in Alberta and 3 in Ontario. Primary care providers described limited experience with personalized medicine, citing breast cancer and prenatal care as main areas of involvement. They expressed concern over their lack of knowledge, in some circumstances relying on personal experiences to inform their attitudes and practice. Participants anticipated an inevitable role in personalized medicine primarily because patients seek and trust their advice; however, there was underlying concern about the magnitude of information and pace of discovery in this area, particularly in direct-to-consumer personal genomic testing. Increased knowledge, closer ties to genetics specialists, and relevant, reliable personalized medicine resources accessible at the point of care were reported as important for successful implementation of personalized medicine. CONCLUSION: Primary care providers are prepared to discuss personalized medicine, but they require better resources. Models of care that support a more meaningful relationship between PCPs and genetics specialists should be pursued. Continuing education strategies need to address knowledge gaps including direct-to-consumer genetic testing, a relatively new area provoking PCP concern. Primary care providers should be mindful of using personal experiences to guide care.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".