Developing leadership competencies for genomics integration through globally networked learning and education
Bibliographic record
Abstract
Aim: To describe the implementation and evaluation of an education strategy called the globally networked learning-genomics (GNL-G) used in Master's courses in Canada and Finland.The study focused on the feasibility and effectiveness of GNL-G in developing leadership competencies for integrating genomics into practice.Methods: Interpretive description qualitative methodology was used to explore how GNL-G, global exchanges and assignments standardized with the Assessment of Strategies to Integrate Genomics in Nursing (ASIGN) tool influenced competency development.The Reporting Item Standards for Education and its Evaluation in Genomics (RISE2 Genomics) framework guided the design, implementation, evaluation, and reporting of GNL-G.Data included one-to-one interviews, written assignments, and reflections.Results: Interviews and assignment data from three cohorts of students for a total of ten Canadian and 11 Finnish master's students participated in this study.The ASIGN Tool played a crucial role in facilitating students' analysis of their practice context and the development of leadership strategies.Participation in GNL-G enhanced students' confidence to lead efforts to integrate genomics, irrespective of their genomics expertise.Engagement with global peers emphasized the importance of incorporating equity, ethics, and social justice into leadership strategies for genomics integration.Conclusion: The GNL-G strategy enhanced leadership competencies for genomics integration in graduate students from Canada and Finland.The ASIGN Tool and global peer collaboration highlight the importance of innovative educational methods in preparing leaders for the complexities of genomics in healthcare.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.184 | 0.060 |
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".