Designing Engagement Strategies for Genomics Informed Oncology Nursing: Comparative Prospective Cross-Jurisidictional Policy Analysis
Bibliographic record
Abstract
The purpose of this project is to generate relevant evidence and policy options to complement advancements in precision healthcare and enable nurses to join genomic health services across Canada. The Canadian Association of Schools of Nursing and the Canadian Association of Nurses in Oncology are the key policy partners. The project is comprised of three key objectives: comparing international genomic nursing policies to those in Canada to determine how policy can align to the quintuple aim, engaging stakeholders to obtain evidence on policy features and drivers that will guide oncology nurses for the safe and equitable integration of genomics across the cancer care continuum, and contextualizing evidence to recommend policy options to guide nursing practice and inter-professional/inter-sectoral collaboration to ensure all Canadians benefit from genomics. To meet these objectives, the project involves three key phases: 1) Phase 1 involves a comparative policy document analysis between Canada, the U.S. and U.K to identify policy features and policy drivers that can accelerate the integration of genomics-informed oncology nursing education and practice. 2) Phase 2 is comprised of 2 steps. Phase 2a involves interviews with patient partners and nurses to understand current and future policy needs and to contextualize phase 1 findings within the Canadian oncology nursing context. Phase 2b involves a virtual policy dialogue with nurses and stakeholders from the multidisciplinary team whose work intersects with genomics to discuss the implications of findings from phase 1 and 2a, and to identify priorities for integrating genomics into nursing education and practice in Canada. 3) Phase 3 involves a workshop with the investigative team and advisory committee to develop policy recommendations and policy briefs for the policy partners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".