The primary care physician role in cancer genetics: a qualitative study of patient experience
Bibliographic record
Abstract
BACKGROUND: Increased availability of genetic testing is changing the primary care role in cancer genetics. The perspective of primary care physicians (PCPs) regarding their role in support of genetic testing has been explored, but little is known about the expectations of patients or the PCP role once genetic test results are received. METHODS: Two sets of open-ended semi-structured interviews were completed with patients (N=25) in a cancer genetic programme in Ontario, Canada, within 4 months of receiving genetic test results and 1 year later; written reports of test results were collected. RESULTS: Patients expected PCPs to play a role in referral for genetic testing; they hoped that PCPs would have sufficient knowledge to appreciate familial risk and supportive attitudes towards genetic testing. Patients had more difficulty in identifying a PCP role following receipt of genetic test results; cancer patients in particular emphasized this as a role for cancer specialists. Still, some patients anticipated an ongoing PCP role comprising risk-appropriate surveillance or reassurance, especially as specialist care diminished. These expectations were complicated by occasional confusion regarding the ongoing care appropriate to genetic test results. CONCLUSIONS: The potential PCP role in cancer genetics is quite broad. Patients expect PCPs to play a role in risk identification and genetics referral. In addition, some patients anticipated an ongoing role for their PCPs after receiving genetic test results. Sustained efforts will be needed to support PCPs in this expansive role if best use is to be made of investments in cancer genetic services.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".