Next-Generation Service Delivery: A Scoping Review of Patient Outcomes Associated with Alternative Models of Genetic Counseling and Genetic Testing for Hereditary Cancer
Bibliographic record
Abstract
The combination of increased referral for genetic testing and the current shortage of genetic counselors has necessitated the development and implementation of alternative models of genetic counseling and testing for hereditary cancer assessment. The purpose of this scoping review is to provide an overview of the patient outcomes that are associated with alternative models of genetic testing and genetic counseling for hereditary cancer, including germline-only and tumor testing models. Seven databases were searched, selecting studies that were: (1) full-text articles published ≥2007 or conference abstracts published ≥2015, and (2) assessing patient outcomes of an alternative model of genetic counseling or testing. A total of 79 publications were included for review and synthesis. Data-charting was completed using a data-charting form that was developed by the study team for this review. Seven alternative models were identified, including four models that involved a genetic counselor: telephone, telegenic, group, and embedded genetic counseling models; and three models that did not: mainstreaming, direct, and tumor-first genetic testing models. Overall, these models may be an acceptable alternative to traditional models on knowledge, patient satisfaction, psychosocial measures, and the uptake of genetic testing; however, particular populations may be better served by traditional in-person genetic counseling. As precision medicine initiatives continue to advance, institutions should consider the implementation of new models of genetic service delivery, utilizing a model that will best serve the needs of their unique patient populations.
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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 | 0.028 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".