Developing Policy Infrastructure to Guide Genomics-Informed Oncology Nursing in Canada: An Interpretive Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Genomic technologies such as genetic testing and precision treatments are rapidly becoming routine in oncology care, and nurses play an increasingly important role in supporting the growing demands for genomics-informed healthcare. Policy infrastructure such as competencies, standards, scope of practice statements, and education and curriculum frameworks are urgently needed to guide these practice and education changes. PURPOSE: This study is part of a larger three-phase project to develop recommendations and catalyze action for genomics-informed oncology nursing education and practice for the Canadian Association of Nurses in Oncology and the Canadian Association of Schools of Nursing. This phase aimed to enhance understanding of policy needs and action drivers for genomics-informed oncology nursing education and practice through the perspectives of Canadian oncology nurses and patient partners. METHODS: Interpretive description methodology guided the study. Twenty semi-structured virtual interviews were conducted; 17 with oncology nurses in various domains of practice, and three with patient partner representatives. Data collection and analysis occurred concurrently. RESULTS: Our analysis identified three themes: 1) nurses and patients recognize that it is time for action, 2) nurses and patients see advantages to executing intentional, strategic, and collaborative policy development, and 3) leadership and advocacy are required to drive action. CONCLUSION: Nursing policy infrastructure is required to increase genomic literacy, support nurses in providing safe patient care, and establish clear roles, responsibilities, and accountabilities within the interdisciplinary team. Strong leadership and advocacy at the practice, organizational, and systems levels are vital to accelerating action.
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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 | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | high |
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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.032 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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, 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".