The Canadian Landscape of Genetics and Genomics in Nursing: A Policy Document Analysis
Bibliographic record
Abstract
BACKGROUND: Genetics and genomics (GG) are transforming approaches to healthcare in Canada and around the globe. Canadian nurses must be prepared to integrate GG in their practice, but modest research in this area suggests that Canadian nurses have limited GG competency. Countries that have integrated GG across nursing provided guidance to nurses about the practice implications of GG through regional nursing policy documents. These documents propelled action to integrate GG across nursing. Little is known about the GG content in the nursing policy document infrastructure in Canada. PURPOSE: This study aimed to examine the guidance for GG-informed nursing practice as provided by Canadian nursing organizations in official professional documents. METHODS: Qualitative document analysis was used. A hybrid inductive/deductive analysis approach was used to analyze findings within the diffusion of innovation theory framework. RESULTS: There is an overall lack of depth and breadth of Canadian nursing documents that include content related to GG. Of the (n = 37) documents analyzed, four themes were generated including (a) GG guidance in nursing education; (b) regulators' requirements for foundational GG knowledge, (c) Canadian Nurses Association (CNA) as an early catalyst to GG integration; and (d) early adopters in speciality practice. CONCLUSION: There are opportunities to enhance the guidance available to Canadian nurses for the application of GG, through documents of nursing professional associations, nursing education accreditation organizations, and regulatory bodies. Findings suggest oncology and perinatal nurses are the early adopters which is an important consideration in future strategies to implement GG into Canadian nursing.
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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 | 0.039 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.036 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".