Large‐scale group genetic counseling: Evaluation of a novel service delivery model in a Canadian hereditary cancer clinic
Bibliographic record
Abstract
Increasing demand for genetic services has led to the development of streamlined genetic counseling (GC) models. We piloted large-scale group pre-test GC with up to 50 patients per group and compared this to a traditional one-on-one approach. Patients referred to the British Columbia (BC) Cancer Hereditary Cancer Program were eligible if they had: (a) family history meeting our program's referral criteria; (b) no relevant personal history of cancer; (c) no prior genetic testing in the family; and (d) no living testable relative in BC. Patient-reported outcome measures included: (a) Genetic Counselling Outcome Scale (GCOS) prior to pre-test GC (T1) and at 4 weeks post-test GC (T2); (b) Satisfaction Survey after pre-test GC; and (c) the Multidimensional Impact of Cancer Risk Assessment (MICRA) for patients undergoing testing (4 weeks after post-test GC). In total, 391 patients underwent GC, 184 by group and 207 by one-on-one appointments. Between May 2018 and May 2019, 6 pre-test group sessions were conducted (median number of patients per group = 28; range 15-48). 8% of patients (n = 32) declined large group GC due to personal preference for one-on-one GC. There were no statistically significant differences in MICRA and GCOS survey results when comparing the pre-test large group versus traditional pre-test one-on-one models (based on 3 MICRA subscales: p = 0.063, p = 0.612, p = 0.842; and GCOS p = 0.169). Overall, the large group pre-test counseling approach was more time-efficient with 15-48 patient group sessions conducted over a mean duration of 80 min as compared to 42 min per patient with the traditional one-on-one GC model. Large-scale group GC was feasible and acceptable to patients and represents a novel streamlined model for GC to enable timely access to 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".