Oncology nurses’ readiness to implement genomics-informed care: A descriptive, cross-sectional study in a Canadian province
Bibliographic record
Abstract
Introduction: Cancer care providers need to be equipped to support cancer care recipients in evolving care contexts. Genomics is an increasingly common component of cancer care. There is limited understanding of Canadian oncology nurses' readiness to contribute to genomics-informed cancer care. Purpose: To describe factors influencing oncology nurses' implementation of genomics in practice in Newfoundland and Labrador (NL; i.e., knowledge, attitudes, confidence, current practices, and social system influences); and identify predictors of their genomic knowledge. Methods: A cross-sectional online survey was administered between September 2023 to February 2024 to nurses working in cancer care in NL. Variables associated with nurses' implementation of genomics-informed practice were measured using the Genomic Nursing Concept Inventory (GNCI©) and select, modified questions from the Genetics Genomics Nursing Practice Survey (GGNPS). Descriptive and inferential statistics were used to report findings. Results: The survey was completed by 50 NL oncology nurses. While 46% of participants indicated that patients had initiated conversations about genomics with them in the past 3 months, their knowledge levels and reported confidence with genomics practices were low overall. Despite this, participants indicated largely positive attitudes toward the benefits of adopting genomics in practice and a willingness to learn more. Conclusion: Findings highlight opportunities to support oncology nurses with practice-based education and resources, to ensure readiness to meet patients' evolving needs and expectations surrounding genomics-informed cancer nursing care.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".