241 Using Nursing Education to Improve the Care of Critically Ill Burn Patients
Bibliographic record
Abstract
The objective of this educational initiative in a 32-bed mixed medical/surgical/trauma intensive care unit with over 240 registered nurses was to improve the quality of care for burn patients. With nearly 1450 yearly admissions, the burn population only represents a small proportion of patients. Critically ill burn patients have complex needs and nurses caring for them are at risk for burn out due to feelings of inadequacy in skill, knowledge, and experience. Critical care nurses identified variation in burn wound management and lack of standardization as a patient care priority. Clinical Nurse Educators from intensive care and the burn unit team met to identify best practices and standardize wound management. A revised burn education program for intensive care was then developed using adult-learning strategies and a multi-disciplinary approach. Program goals consisted of developing knowledge of burn wound assessment and healing, standardizing practice, building confidence, and implementing self-assessment for nurses and other staff. A self-assessment competency tool is completed by each nurse and reviewed with the intensive care educators; strategies for further professional development are then highlighted. Burn resources are available in key locations such as the burn dressing cart and informational unit board. A burn chart pack has been developed and electronic materials are underway. Written evaluations have identified positive uptake from the revised burn education program with course participants reporting an increased level of confidence and enthusiasm for burn wound management. A collaborative approach to burn education can bridge gaps in practice and enhance patient-centered care throughout the illness trajectory. Current, adult-focused learning strategies that are adapted to the needs of the learners improved the uptake of best practices. Carving out a niche for burn patients in a mixed intensive care unit can be done with a collaborative multi-disciplinary approach. Seeking input from key stakeholders and identifying needs of learners to then develop a sustainable educational program assists in translating knowledge into practice and developing confidence among nursing when caring for this complex population. Continued evaluation of such programs and the willingness to adapt and change as required is vital for educational programs to be successful.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".