Education needs of nurses in thrombosis and hemostasis: An international, mixed‐methods study
Bibliographic record
Abstract
BACKGROUND: The need for a more integrated, multidisciplinary approach to care for individuals with bleeding or clotting disorders has been highlighted in recent years. Evidence-based education adapted to nurses' needs is essential for a successful evolution. However, limited data currently exist on the clinical challenges nurses face in this specialty area. OBJECTIVES: Identify barriers and challenges faced by specialty nurses, and determine possible causes, to develop appropriate educational interventions. METHODS: A mixed-methods study, combining qualitative (semi-structured interviews) and quantitative (online survey) data was conducted on the challenges experienced by hemostasis nurses in nine countries (Argentina, Australia, Canada, China, France, Germany, Spain, the UK, and the US), and deployed in five languages (English, French, German, Mandarin, and Spanish). Qualitative data were analyzed using thematic analysis. Quantitative data were analyzed using frequency tables, chi-squares and standard deviations. RESULTS: Participants (n = 234) included nurses (n = 212; n = 22 qualitative; n = 190 quantitative); and patients receiving care for bleeding or clotting conditions or their caretakers (n = 22 qualitative phase only). Through triangulated data analysis, six challenging areas emerged: (a) Understanding of von Willebrand disease (VWD); (b) Anticoagulant safety profile in specific patients; (c) Understanding the treatment of patients with inhibitors; (d) Patient risk assessments; (e) Individualization of care and communication with patients; and (f) Accessing and implementing relevant professional education. CONCLUSIONS: This needs assessment provides a comprehensive illustration of the current challenges faced by nurses in the field of bleeding and clotting disorders, and indicates where gaps in skills, knowledge or confidence would benefit from nurse-specific educational programming.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".