Evidence summary and application of delirium risk assessment and prevention in elderly patients in emergency non⁃intensive care settings
Bibliographic record
Abstract
ObjectiveTo investigate the current application status of evidence on delirium risk assessment and prevention in elderly emergency department patients,to analyze the barriers and facilitators in evidence⁃based nursing practice, and provide a basis for the clinical translation of evidence.MethodsUsing the Ottawa Model of Research Application as the theoretical framework,audit criteria and methods were developed based on best evidence.Barriers and facilitators to evidence implementation in clinical practice were analyzed from three aspects:evidence-based change,potential adopters,and practice environment,based on clinical audits and interviews with stakeholders.ResultsA total of 22 pieces of evidence were included,leading to the formulation of 22 audit criteria.The audit results showed that compliance rates for 13 criteria were 0%,6 criteria had compliance rates below 60%,and the remaining 3 criteria had compliance rates above 60%.The evidence⁃based changes for delirium risk assessment and prevention were complex.Main barriers included:the relevant evidence not being integrated into routine nursing care in the emergency department;a lack of professional knowledge and skills among emergency healthcare staff regarding delirium risk assessment and prevention;numerous risk factors for delirium in elderly emergency patients;the complex emergency environment;a lack of standardized screening tools,assessment and prevention protocols,and systems;and low rates of delirium education.Key facilitators included:a well⁃established hospital evidence⁃based project management system,leadership support,strong willingness for change,and alignment of the changes with quality control management requirements of the healthcare system.ConclusionsThere is a significant gap between the evidence on delirium risk assessment and prevention for elderly emergency patients and clinical practice.Analyzing the barriers and facilitators could help developing further improvement measures to promote the effective application of evidence.
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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 | 0.048 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".