Riding the wave of change: Providing solid ground to support nursing with patient transitions to novel haemophilia therapies
Bibliographic record
Abstract
INTRODUCTION: Haemophilia nursing practice has experienced a shift in the past decade, as the historic chief focus on factor infusions shifted to extended half-life products, bispecific antibody therapies and other non-replacement therapies. This evolution has driven a need for changes in nursing practice in many haemophilia treatment centres. AIM: This article intends to provide insights to the haemophilia nurse to champion practice changes at their haemophilia treatment centres. METHODS: Two popular change theories, Lewin's three-step change model and Kotter's eight-step change model are discussed as a framework for haemophilia nurses to think, structure and be leaders in change. CONCLUSION: Examples of these models in practice could give guidance and examples to reflect on for haemophilia nurses needing to make changes in their practice settings. These models of change, alongside existing haemophilia nurse competencies and tools such as the shared decision-making tool from the World Federation of Hemophilia, can assist the nurse to be a capable change agent to usher in these new innovations.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".