Adopting clinical genomics: a systematic review of genomic literacy among physicians in cancer care
Bibliographic record
Abstract
BACKGROUND: This article investigates the genomic knowledge of oncology care physicians in the adoption of clinical genomics. We apply Rogers' knowledge framework from his diffusion of innovation theory to identify three types of knowledge in the process of translation and adoption: awareness, how-to, and principles knowledge. The objectives of this systematic review are to: (1) examine the level of knowledge among physicians in clinical cancer genomics, and (2) identify potential interventions or strategies for development of genomic education for oncology practice. METHODS: We follow the PRIMSA statement protocol and conduct a search of five relevant electronic databases. Our review focuses on: (1) genomic knowledge of oncogenomics or genomic services in oncology practices among physicians, and (2) interventions or strategies to provide genomic education of oncogenomics for physicians. RESULTS: We include twenty-one studies in our analysis. Nine focus on interventions to provide genomic education for cancer care. Overall, physicians' knowledge of oncogenomics among the three types is limited. The genomic literacy of physicians vary by their provider specialty, location, years of practice, and the type of genomic services. The three distinctions of knowledge offer a sophisticated and helpful tool to design effective strategies and interventions to provide genomic education for cancer treatment. In the nine educational intervention studies, the main intervention outcomes are changes in awareness, referral rates, genomic confidence, and genomic knowledge. CONCLUSION: Rogers' diffusion of innovation model allows us to differentiate three types of knowledge in the development and adoption of clinical genomics. This analytical lens can inform potential avenues to design more effective strategies and interventions to provide genomic education for oncology practice. We identified and synthesized a dearth of high quality studies that can inform the most effective educational outcomes of these interventions. Future research should attend to improving applications of genomic services in clinical practices, along with organizational change engendered by genomics in oncology practice.
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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 | 0.020 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".