Early recognition and treatment of pediatric sepsis: the development of an education resource for registered nurses
Bibliographic record
Abstract
Background: Sepsis is a potentially fatal condition and is a major cause of mortality among pediatric populations. Delayed recognition of sepsis symptoms and delayed or insufficient treatment have been identified as contributing factors to increased mortality rates among pediatric patients with sepsis. Due to their frequent interactions with patients, research has shown that providing nurses with education about the signs and symptoms of sepsis and evidence-based sepsis treatments can significantly improve patient outcomes. Purpose: The purpose of this practicum was to develop a self-learning for registered nurses in NL to improve their knowledge and understanding of the signs and symptoms of sepsis and evidence-based sepsis treatment guidelines. Methods: Three methodologies were used in this practicum. These methodologies included an integrated literature review, a series of consultations with key stakeholders, and an environmental scan to review other educational resources on sepsis. Results: An online educational module on pediatric sepsis was developed using the information collected from the literature review and consultations. The module was developed using the eportfolio program available through Memorial University of Newfoundland and Labrador’s desire2learn (D2L) website. The module consisted of three units describing sepsis and nursing, the symptoms of sepsis, and treating sepsis. Conclusion: The goal of this practicum was develop an educational resource to increase nurses’ knowledge of sepsis to improve their recognition of sepsis symptoms and their compliance with evidence-based treatment guidelines. The module was not piloted during this practicum, however, future evaluation plans have been developed.
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".