Decision makers' perceptions of integrating genetic counselors into primary care
Bibliographic record
Abstract
BACKGROUND: Over the past decade, there have been rapid advancements in genomic medicine that have improved the clinical utility of genetic testing and counseling. Although theoretically, primary care is an ideal locations for the delivery of genomic medicine, physicians lack training, confidence, and time to implement these services. Though it has been suggested that genetic counselors could be integrated into the primary care setting to fill this gap in care, such integration has not yet been widely implemented. Decision makers' perceptions are foundational in the implementation of this model, but have yet to be explored. METHODS: We conducted a qualitative interview-based study with individuals holding key administrative roles in primary care systems to elicit opinions on a model of care that integrates genetic counselors into primary care. Interviews were analyzed using interpretive description involving thematic coding and iterative discussions among the research team to develop a comprehensive conceptual model. RESULTS: Barriers included team integration difficulties, a perceived lack of buy-in at the institutional, physician, and patient levels, and a lack of resources. Participants felt that integrating genetics into primary care is most feasible when one genetic counselor is contracted as a resource to multiple different clinics and is able to provide remote or hybrid care. CONCLUSION: Despite the growing evidence supporting the integration of genetic counselors into primary care settings, decision makers have concerns about how this integration will occur, and feel that more buy in is needed from patients, providers, and administration to make this model of care a reality.
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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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".