Modes of delivery of genetic testing services and the uptake of cancer risk management strategies in <i>BRCA1</i> and <i>BRCA2</i> carriers
Bibliographic record
Abstract
BRCA testing services are now offered by various healthcare providers, thus it is important to evaluate whether the implementation of cancer risk management (CRM) strategies varies by service provider. Using a registry-based sample of 795 female BRCA mutation carriers, we explored the association between uptake of CRM strategies with duration of genetic counseling (GC) sessions, provider type, and other demographic and clinical variables. All participants completed a baseline questionnaire. Information about uptake of CRM strategies was collected for a subset of 438 participants who completed additional questions. Summary statistics and Pearson chi-squared analysis were used to examine the associations between demographic and clinical variables with service delivery factors and with the uptake of various CRM strategies. Overall uptake of CRM strategies was high across all provider types. However, GC sessions were longer when provided by a genetics professional than by another provider (p < 0.001). Furthermore, higher frequencies of uptake of most CRM strategies were associated with longer GC sessions and when testing was performed by a genetics professional. Identification of factors to optimize delivery of these specialized GC services is important to maximize implementation of CRM strategies in BRCA carriers.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".