Barriers and Facilitators to Genetic Service Delivery Models: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Advances in diagnostics testing and treatment of genetic conditions have led to increased demand for genetic services in the United States. At the same time, there is a shortage of genetic services professionals. Thus, understanding the models of service delivery currently in use can help increase access and improve outcomes for individuals identified with genetic conditions. OBJECTIVE: This review aims to provide an overview of barriers and facilitators to genetic service delivery models to inform future service delivery. METHODS: We conducted a scoping literature review of the evidence to more fully understand barriers and facilitators around the provision of genetic services. RESULTS: There were a number of challenges identified, including the limited number of genetics specialists, wait time for appointments, delivery of services by nongenetics providers, reimbursement, and licensure. The ways to address these challenges include the use of health information technology such as telehealth, group genetic counseling, provider-to-provider education, partnership models, and training; expanding genetic provider types; and embedding genetic counselors in clinical settings. CONCLUSIONS: The literature review highlighted the need to expand access to genetic services. Ways to expand services include telehealth, technical assistance, and changing staffing models. In addition, using technology to improve knowledge among related professionals can help expand access.
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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 | 0.057 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".