Innovative approaches to teaching vascular access to nursing students in the COVID-19 era
Bibliographic record
Abstract
PURPOSE: For the student nurse, peripheral venous cannulation is one of the most stressful skills to be learned. Although some healthcare employers/establishments offer courses on vascular access and infusion nursing as part of their onboarding programs, ultimately educational institutions should share the responsibility to ensure that graduating nurses can provide safe infusion therapies. METHODS: An innovative vascular access and infusion nursing (VAIN) curriculum was created and mapped onto the entry to practice undergraduate nursing program at McGill University in Montréal, Québec, Canada. This presented an opportunity to implement new teaching approaches. RESULTS: Students experienced multiple new teaching approaches including multimedia and experiential learning and live simulation to ensure acquisition of knowledge and psychomotor skills. The teaching approaches had to be rapidly modified with the advent of the COVID-19 pandemic. CONCLUSIONS: The VAIN curriculum emphasizes simulation and directed practice, seeking to increase competence, confidence, and knowledge. The pandemic underscored the need for flexibility and creativity in content delivery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".