Perspectives: Implementation strategies to adopt and integrate evidence-based nursing. What are we doing?
Bibliographic record
Abstract
The recently-articulated vision for the future of nursing in the Future of Nursing report (Robert Wood Johnson Foundation Committee Initiative on the Future of Nursing, 2011) focuses on the convergence of knowledge, quality, and new functions in nursing. The recommendation that nurses should lead interprofessional teams in improving delivery systems and care, brings to the front the necessity for new competencies, beyond evidence-based practice (EBP), that are required by nurses to transform healthcare. These competencies focus on utilising knowledge in clinical decision making and on producing research evidence on interventions that promote uptake and use by individual practitioners. The European Strategic Directions for Strengthening Nursing and Midwifery Towards Health 2020 Goals (World Health Organisation Regional Office for Europe, 2015) is a technical document setting out agreed actions to be taken to support the implementation of Health 2020 by the nursing and midwifery professions. This document points out four priority action areas necessary to support nurses and midwives in contributing effectively to the health of their communities. One of the action areas is promoting EBP and innovation. In this matter, the recommendation for all Member States is to enable their nurses and midwives to apply EBP in their clinical roles and in decision-making involving patient care. EBP holds great promise for moving care to a position where it is more likely to produce the health outcomes intended. In order to cross the chasm between what we know to be effective healthcare and what is practised we need to be using evidence to inform best practices. Dissemination and implementation of research is a growing area of science focused on overcoming this science-practice gap. Implementation science is the study of methods to promote the integration of evidence into practice and health care policy within real-world public health and clinical service settings (National Institutes of Health, Fogarty International Center, 2010). The delay in turning research into practice for the benefit of
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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 | 0.189 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.035 | 0.043 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.035 | 0.037 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".