Genetic literacy among surgeons who manage patients with cancer: a national survey of knowledge, perceptions, attitudes, and barriers
Bibliographic record
Abstract
BACKGROUND: The rapid evolution of genetic testing and availability of information has necessitated increased surgeon participation in genetics-related tasks. We sought to characterize knowledge, perceptions, attitudes, and barriers pertaining to genetic literacy among Canadian surgeons who manage patients with a hereditary predisposition to or confirmed cancer. METHODS: We distributed a Web-based survey to surgeons across Canada from June to December 2023 through relevant surgical societies. We analyzed quantitative and narrative data from the survey descriptively and thematically. RESULTS: We included 57 participants from 8 provinces (response rate 10%). Many surgeons (28/45, 62%) reported performing risk assessment, but 16% (7/45) reported counselling and 29% (13/45) reported ordering genetic testing. Surgeons reported low confidence in ordering testing and in interpreting and discussing implications of testing results. Most surgeons (35/39, 90%) expressed a desire for improvement in their knowledge and in their confidence in hereditary cancer genetics. Approval and funding for testing, referral to a genetic counsellor or medical geneticist, and availability of genetics clinics were reported as extreme barriers to providing care. CONCLUSION: Practising surgeons in Canada participate in many genetics-related tasks, but they report low confidence and face barriers to genetic literacy. There is a need and desire for interventions targeting genetic literacy among surgeons in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".