Translating evidence into practice: Developing Canada’s first position statement on genomics-informed oncology nursing
Bibliographic record
Abstract
The integration of genomics in oncology care is accelerating in Canada, presenting new opportunities for nurses to improve cancer outcomes through enhanced screening, diagnosis, and targeted therapies. However, nurses have identified that they require policy guidance to clarify their roles and responsibilities as members of interprofessional teams delivering genomic services. In response, the Canadian Nursing and Genomics Initiative, in collaboration with the Canadian Association of Nursing in Oncology/Association Canadienne des Infirmières et Infirmiers en Oncologie, and an interdisciplinary working group developed the first pan-Canadian position statement to guide genomics-informed oncology nursing practice. To support further engagement and use of the position statement, we outline the rationale for developing the position statement and the role of position statements in supporting nursing practice and innovation in genomics-informed oncology nursing. We describe the governance structure and co-design methodology that facilitated its collaborative interdisciplinary development, and how this approach is critical for nursing advocacy, integrated knowledge translation, and ongoing engagement. Finally, we offer recommendations for oncology nurses to translate the position statement into practice changes. This position statement is a preliminary step toward advancing genomics integration in cancer care and ensuring nursing practice remains at the forefront of innovation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: yes · About a Canadian topic: no | Not applicable | medium |
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 | 0.185 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".