Mapping the Disaster Competency Landscape in Undergraduate Nursing - A Case Study of Nursing Educators in British Columbia, Canada
Bibliographic record
Abstract
Introduction: In British Columbia (BC), Canada, it is increasingly commonplace for communities to experience yearly disaster events such as floods, forest fires, avalanches, and mudslides. Nurses are known to be one of the largest groups of healthcare workers and are often challenged to care for members of the public during these events. Many nurses have stated that they do not have enough education to provide quality care in a disaster role, as they received no education in their undergraduate nursing degrees. Aim: The aim of this study was to explore how and what nurse educators are teaching undergraduate nursing students regarding the disaster nursing role within Schools of Nursing in BC, Canada. Understanding the current practice of teaching will serve as a starting point for shaping future best practice undergraduate nursing disaster educational frameworks. Methods: This study used a qualitative case study methodology guided by Merriam’s procedural approach with a theoretical framework of adult teaching and learning. Results: The findings indicate that disaster nursing knowledge is taught either within existing global health courses or rarely is leveled throughout the program. Many challenges exist for educators, which include lack of current resources, workload restrictions, and lack of personal disaster knowledge. Content is determined by the educator. However, there is no specific model or link to disaster nursing competencies or assessment strategies. Most content is delivered didactically by the educator with some expert guest speakers or collaborative simulation events. Discussion: The identified priority challenge is to obtain clarity and understanding around just what knowledge is required and how it should be evaluated. Some suggestions for a specific undergraduate disaster nursing model will be presented in order to ensure that students have the foundational knowledge that they require and that our educators are prepared to teach them.
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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 | 0.000 |
| 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 | 0.000 | 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".