Implementation Mapping to Identify Best Practices for Implementing Population-Wide Genomic Screening Programs: Protocol for the FOCUS (Facilitating the Implementation of Population-Wide Genomic Screening) Study
Bibliographic record
Abstract
Background: Population-wide genomic screening (PGS) for genetic conditions such as hereditary breast and ovarian cancer syndrome, Lynch syndrome, and familial hypercholesterolemia presents opportunities to reduce morbidity and mortality among the 1%-2% of the population at elevated risk for these serious, preventable diseases. With decreasing sequencing costs and growing support from national bodies, there are increasing numbers of PGS programs in the United States. However, guidelines and strategies to support implementation are limited, especially regarding equitable access to PGS. Contextual factors, such as organizational structures and processes, impact PGS implementation, often failing to benefit underrepresented populations. To address these challenges, we are completing the Facilitating the Implementation of Population-wide Genomic Screening (FOCUS) project, which will develop and test a freely available, web-based implementation toolkit to guide best practices for implementing PGS. Objective: The FOCUS project aims to (1) examine barriers and facilitators of PGS implementation at diverse health systems, (2) develop implementation strategies with input from an advisory panel and package them into the FOCUS toolkit, and (3) evaluate the toolkit's impact on improving PGS reach, effectiveness, adoption, and maintenance using a hybrid stepped-wedge cluster randomized trial design. Methods: We will complete implementation mapping, guided by the Consolidated Framework for Implementation Research integrated with health equity, and the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework for Health Equity to develop and evaluate an equity-focused PGS implementation toolkit. The study will involve 10 design sites to identify implementation barriers and facilitators and 12 Test Sites to assess the toolkit's effectiveness. Both design and test sites will be representative of the following 4 stages of implementation: exploration or emerging, planning, implementation, and sustainment. Results: The FOCUS project was funded in September 2024 and will conclude in June 2029. The project was funded through the Advancing Genomic Medicine Research Program at the National Human Genome Research Institute (R01HG013851-01). Data collection for aim 1 (qualitative interviews with implementation team members, patients, and laboratory vendors) began January 2024. At the time of reporting, 33 interviews have been completed with implementation team members, 8 with patients, and two with laboratory vendors. Qualitative analyses for aim 1 are underway at the time of reporting. Conclusions: The FOCUS toolkit will establish a standardized approach to scaling PGS programs across diverse populations and settings, ensuring genomics benefits are accessible to all.
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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 | 0.156 | 0.138 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.014 |
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".