Managing Cytotoxic Extravasation: Simulation-based tutorials to improve quality of care and patient safety for chemotherapy patients
Bibliographic record
Abstract
This paper reports on a quality improvement project that was conducted collaboratively by an oncology clinical nurse coordinator and simulation nurse educator. The project utilized simulation- based tutorials with a stepwise approach to train and evaluate the self-efficacy of oncology registered nurses working in a tertiary care hospital. A general assessment of the problem of cytotoxic extravasation was explored using an Ishikawa diagram and the factors responsible for such common issues in chemotherapy administration and oncology nursing practice were plotted. The Plan- Do-Study-Act (PDSA) approach was utilized to apply the quality improvement project method and to strategize the learning for the oncology nurses. The simulation tutorials approach used was an innovative educational activity. The project was completed by training each oncology registered nurse to enhance their efficacy in identifying and managing extravasation. The innovation of simulation-based education tutorials was effective in enhancing nurses' practices. It also provided insight for continuous nursing education and evaluation that will impact the clinical practice routines in oncology care, thus saving patients from potential adverse effects of chemotherapy.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".