Evidence-based nursing management: Challenges and facilitators
Bibliographic record
Abstract
This review introduces a new approach for nursing management called Evidence Based Nursing Management (EBNMgt). EBNMgt is the integration of the best research evidence with nurse managers’ expertise and nurses’ preferences. Even though it is commonly acknowledged that health care should be evidence-based, evidence-based decision making in the field of nursing management has not been addressed adequately in terms of what actually determines the quality of nursing care and working conditions of nurses. This review discusses the significance of evidence-based nursing management from the perspective of both academics and managers, and elaborates on both the difficulties of implementing evidence-based nursing management and ways of facilitating implementation. Collaborative models between universities and hospitals could be used as a method to facilitate the implementation of evidence-based nursing management in order to improve nursing care and the working environment. Like evidence-based nursing, evidence-based nursing management should also be carefully studied for the professionalization of nursing and for better patient care.
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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 | 0.472 | 0.442 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 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".