The Competency Framework for Genomics Nurse Educators: An International Delphi Study to Advance Global Nursing Education
Bibliographic record
Abstract
AIM: To validate a Competency Framework for Genomics Nurse Educators, strengthening the genomics-informed nursing workforce. BACKGROUND/INTRODUCTION: A pressing global need for nurse educators equipped to build genomic literacy within the profession prompted the International Society of Nurses in Genetics to develop the Competency Framework for Genomic Nurse Educators. METHODS: A draft framework was refined using literature and stakeholder input guided by an evidence-based model for competency framework development. A modified Delphi study with an international panel of genomics nurse experts completed a three-round Delphi study. Methodology and reporting adhere to EQUATOR guidelines using the CONFERD-HP and CREDES checklists. RESULTS: A 24-member panel-with genomic nursing expertise spanning 13 countries-validated a set of competency statements organized into three domains covering foundational knowledge, genomics-specific teaching approaches, and contribution to the discipline through leadership and academic-practice partnerships. DISCUSSION: These competencies address essential aspects of genomics nursing education and respond to increasing demand for specialization within academic nursing education. CONCLUSION: The validated framework addresses gaps in global genomic nursing education. This framework supports a focused approach to genomics education, preparing a new generation of nurse educators to meet the challenges of precision health. IMPLICATIONS FOR NURSING: Implementing this framework can standardize genomics education, strengthen faculty development, and ensure nurses are prepared to practice genomics-informed nursing. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Completion of the framework affirms genomics nursing education as a distinct specialty, laying groundwork for formal credentialing and shaping education priorities. Increasing the number of nurse educators with genomic expertise can strengthen academic-practice partnerships and foster global collaboration, ultimately supporting the equitable integration of precision health in nursing worldwide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".