Evaluation of Applications of Adaptation of the Evidence-Based Nursing Practice Guidelines Patients with Acute Stroke
Bibliographic record
Abstract
Purpose: This study was done to evaluate nursing guidelines for patients with acute stroke, developed by adapting the guidelines of Registered Nurses Association of Ontario, Canada to clinical settings on a large scale and evaluating the effectiveness as a research study. Method: The general characteristics of the 319 patients and the effectiveness of guideline application were evaluated in terms of structure, process, and outcome using questionnaires on the guidelines application with reference to the medical records of patients with acute stroke hospitalized on a ward of the stroke center of S General Hospital in Seoul. Results: Structures as a guidance system for assessment were consistent with the recommendations. With respect to the process of the guidelines, for items on nursing assessment, improved performance was found to be statistically significant. For outcomes of the guidelines, complications occurred in 8 patients (5.3%) prior to application of the guidelines and 11 patients (6.5%) after application of the guidelines, but this result was not statistically significant (p=.841). Conclusion: The results of the study indicate that for the effectiveness of the guidelines, accessibility to the guidelines and effectiveness of quality improvement need to be evaluated, in addition to complications of a stroke.
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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 | 0.028 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".