The experiences of emergency nurses working during situations of crowding
Bibliographic record
Abstract
Background: While the causes and consequences of patient crowding in emergency departments are clearly defined, little is known about the experiences of emergency nurses working in these conditions.The aim of this study was to develop a greater understanding of emergency nurses' experiences working during situations of crowding.Methods: Fourteen emergency nurses were individually interviewed.Data was analyzed using a qualitative descriptive approach.Results: Five major themes were identified: challenges of the ED environment, impacts of the environment on practice, professional impacts, impacts on person, and teamwork: the silver lining.Participants described crowding as occurring on a consistent and regular basis; this had both negative and positive impacts upon their professional and personal lives.Discussion: Emergency nurses are persistent in their desire to provide quality patient care, despite situations of crowding.Future implications involve identifying positive practice supports and providing education on self-care practices to foster positive personal health outcomes.iii Table of ContentsAbstract……………………………………………………………………………………… ii Table of Contents…………………………………………………………………………… iii List of Tables………………………………………………………………………………... vi List of Figures……………………………………………………………………………… vii Acknowledgements………………………………………………………………………... viii
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".