Genetic Services for Hereditary Breast/Ovarian and Colorectal Cancers – Physicians’ Awareness, Use and Satisfaction
Bibliographic record
Abstract
OBJECTIVES: In 2000, the Ministry of Health in Ontario, Canada, introduced a publicly funded program to provide genetic services for hereditary breast/ovarian and colorectal cancers. We surveyed physicians to determine their awareness, use and satisfaction with this program. METHODS: A self-administered questionnaire was mailed to a random sample of 25% of Ontario family physicians and all gynecologists, oncologists (radiation, surgical and medical), gastroenterologists and general surgeons. RESULTS: Response rate was 49% (n = 1,427). Awareness of genetic testing for breast/ovarian cancer was high (91%) but less for colorectal cancer (60%). Use of services was associated with physician age of 40 or greater, urban location, confidence in knowledge of referral criteria and core competencies in genetics, and awareness of the program and where to refer. Almost half were dissatisfied with notification about the program. CONCLUSIONS: Ontario physicians are aware of cancer genetics services, and use is associated with increased knowledge of services, and confidence in skills. They would like more timely services and education about hereditary cancers and susceptibility testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".