Integrating genomics into Canadian oncology nursing policy: Insights from a comparative policy analysis
Bibliographic record
Abstract
AIM: To learn from two jurisdictions with mature genomics-informed nursing policy infrastructure-the United States (US) and the United Kingdom (UK)-to inform policy development for genomics-informed oncology nursing practice and education in Canada. DESIGN: Comparative document and policy analysis drawing on the 3i + E framework. METHODS: We drew on the principles of a rapid review and identified academic literature, grey literature and nursing policy documents through a systematic search of two databases, a website search of national genomics nursing and oncology nursing organizations in the US and UK, and recommendations from subject matter experts on an international advisory committee. A total of 94 documents informed our analysis. RESULTS: We found several types of policy documents guiding genomics-informed nursing practice and education in the US and UK. These included position statements, policy advocacy briefs, competencies, scope and standards of practice and education and curriculum frameworks. Examples of drivers that influenced policy development included nurses' values in aligning with evidence and meeting public expectations, strong nurse leaders, policy networks and shifting healthcare and policy landscapes. CONCLUSION: Our analysis of nursing policy infrastructure in the US and UK provides a framework to guide policy recommendations to accelerate the integration of genomics into Canadian oncology nursing practice and education. IMPLICATIONS FOR THE PROFESSION: Findings can assist Canadian oncology nurses in developing nursing policy infrastructure that supports full participation in safe and equitable genomics-informed oncology nursing practice and education within an interprofessional context. IMPACT: This study informs Canadian policy development for genomics-informed oncology nursing education and practice. The experiences of other countries demonstrate that change is incremental, and investment from strong advocates and collaborators can accelerate the integration of genomics into nursing. Though this research focuses on oncology nursing, it may also inform other nursing practice contexts influenced by genomics.
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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 | 0.042 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.030 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".