Abstract 463: Communicating Through Chaos: A Hybrid Educational Initiative for Urgent Care Oncology Nurses
Bibliographic record
Abstract
Introduction: Cancer and cardiovascular disease are two leading causes of death in North America and can occur concurrently in oncology patients. Novel cancer treatments can cause direct cardiovascular side effects, increasing patient mortality. We aimed to educate oncology nurses of ST-Elevated Myocardial Infarction (STEMI) recognition and treatment using a hybrid educational model of didactic and simulation methods with contextual learning. Hypothesis: We hypothesize that a hybrid model including didactic and simulation methodology would result in increased knowledge, skills, and attitudes in the management of patients with STEMI. This would lead to decreased STEMI-related morbidity and improve interprofessional care co-ordination. Methods: Education was provided to 71% (5/7) of Registered Nurses in the Urgent Care Centre of Princess Margaret Hospital in Toronto, Canada. From a unit-wide needs assessment, RNs determined that patient management, care co-ordination and communication skills were areas of educational need, and simulation and lectures were preferred education methods. Current evidence, treatment, and policies for STEMI were incorporated into a short lecture, followed immediately by in situ high fidelity simulation scenarios. All education incorporated the AHA principle of contextual learning to support clinically-relevant knowledge acquisition. Educational gain was assessed using pre- and post-education testing. Results: Following the educational intervention, all learners had an increase in post-test scores. Recognition of the need for an ECG improved by 60% (3/5) and nurses felt more confident in caring for STEMI patients (3.6/4 average score). Learners found the education beneficial and relevant to their practice (100% of respondents). Nurses emphasized the value of didactic education followed by skills application in simulated cases as valuable to their learning from qualitative survey feedback. Conclusion: We developed and implemented a highly-relevant hybrid STEMI educational initiative following a needs assessment. Learners exhibited knowledge acquisition through pre and post-test results. Data collection regarding door-to-ECG and door-to-diagnosis times post-education is ongoing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".