Assessing the Management of Gastric Bleeding: Survey of Nursing Professionals at a Tertiary Care Hospital
Bibliographic record
Abstract
This study aims to gather insights from nursing professionals involved in managing gastric bleeding to identify strengths and areas for improvement in current practices, ultimately informing the development of enhanced protocols for care management and interdepartmental transfers. A survey was conducted targeting registered nurses working in the endoscopy department at St. Michael’s Hospital in Canada. The survey assessed nursing experiences and perceptions regarding the management of gastric bleeding, protocol availability, and communication practices between endoscopy and interventional radiology departments. A total of 26 responses were analyzed to provide a comprehensive overview of current practices. Respondents highlighted that while most nurses are trained to recognize early signs of gastric bleeding, there are no established protocols for managing such cases or for transferring care between departments. The average satisfaction rating for existing management protocols was 5.6 out of 10, indicating significant room for improvement. Key challenges identified included inconsistent emergency protocols, lack of standardized training, and inadequate resources during patient transfers. Effective communication was rated highly (8/10) within teams, yet transfer processes were marked by insufficient documentation quality (5/10). Respondents emphasized the importance of clear communication, teamwork, and having established protocols to enhance patient safety and care coordination. The findings reveal critical gaps in the management of gastric bleeding, particularly concerning protocol establishment and interdepartmental communication. By addressing these gaps and fostering a standardized approach to training and patient care processes, we can improve outcomes for patients experiencing gastric bleeding during endoscopic procedures. Future work will focus on developing and implementing evidence-based protocols to enhance the preparedness and efficacy of nursing staff in managing these complex situations. Publication History Article published online: 27 March 2025 © 2025. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".