Bridging the gap in genetics: a progressive model for primary to specialist care
Bibliographic record
Abstract
Abstract Background The rapid expansion of genetic knowledge, and the implications for healthcare has resulted in an increased role for Primary Care Providers (PCPs) to incorporate genetics into their daily practice. The objective of this study was to explore the self-identified needs, including educational needs, of both urban and rural Primary Care Providers (PCPs) in order to provide genetic care to their patients. Methods Using a qualitative grounded theory approach, ten key informant interviews, and one urban and two rural PCP focus groups (FGs) (n = 19) were conducted. All PCPs practiced in Southeastern Ontario. Data was analyzed using a constant comparative method and thematic design. The data reported here represent a subset of a larger study. Results Participants reported that PCPs have a responsibility to ensure patients receive genetic care. However, specific roles and responsibilities for that care were poorly defined. PCPs identified a need for further education and resources to enable them to provide care for individuals with genetic conditions. Based on the findings, a progressive stepped model that bridges primary and specialty genetic care was developed; the model ranged from PCPs identifying patients with genetic conditions that they could manage alone, to patients who they could manage with informal or electronic consultation to those who clearly required specialist referral. Conclusions PCPs identified a need to integrate genetics into primary care practice but they perceived barriers including a lack of knowledge and confidence, access to timely formal and informal consultation and clearly defined roles for themselves and specialists. To address gaps in PCP confidence in providing genetic care, interventions that are directed at accessible just-in-time support and consultation have the potential to empower PCPs to manage patients’ genetic conditions. Specific attention to content, timing, and accessibility of educational interventions is critical to address the needs of both urban and rural PCPs. A progressive framework for bridging primary to specialty care through a ‘stepped’ model for providing continuing medical education, and genetic care can was developed and can be used to guide future design and delivery of educational interventions and resources.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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".