Implementation strategies used to implement nursing guidelines in daily practice: A systematic review
Bibliographic record
Abstract
OBJECTIVES: Research specifically addressing implementation strategies regarding nursing guidelines is limited. The objective of this review was to provide an overview of strategies used to implement nursing guidelines in all nursing fields, as well as the effects of these strategies on patient-related nursing outcomes and guideline adherence. Ideally, the findings would help guideline developers, healthcare professionals and organizations to implement nursing guidelines in practice. DESIGN: Systematic review. PROSPERO registration number: CRD42018104615. DATA SOURCES: We searched the Embase, Medline, PsycINFO, Web of Science, Cochrane, CINAHL and Google Scholar databases until August 2019 as well as the reference lists of relevant articles. REVIEW METHODS: Studies were included that described quantitative data on the effect of implementation strategies and implementation outcomes of any type of a nursing guideline in any setting. No language or date of publication restriction was used. The Cochrane Effective Practice and Organisation of Care taxonomy was used to categorize the implementation strategies. Studies were classified as effective if a significant change in either patient-related nursing outcomes or guideline adherence was described. Strength of the evidence was evaluated using the 'Cochrane risk of bias tool' for controlled studies, and the 'Newcastle-Ottawa Quality Assessment form' for cohort studies. RESULTS: A total of 54 articles regarding 53 different guideline implementation studies were included. Fifteen were (cluster) Randomized Controlled Trials or controlled before-after studies and 38 studies had a before-after design. The topics of the implemented guidelines were diverse, mostly concerning skin care (n = 9) and infection prevention (n = 7). Studies were predominantly performed in hospitals (n = 34) and nursing homes (n = 11). Thirty studies showed a positive significant effect in either patient-related nursing outcomes or guideline adherence (68%, n = 36). The median number of implementation strategies used was 6 (IQR 4-8) per study. Educational strategies were used in nearly all studies (98.1%, n = 52), followed by deployment of local opinion leaders (54.7%, n = 29) and audit and feedback (41.5%, n = 22). Twenty-three (43.4%) studies performed a barrier assessment, nineteen used tailored strategies. CONCLUSIONS: A wide variety of implementation strategies are used to implement nursing guidelines. Not one single strategy, or combination of strategies, can be linked directly to successful implementation of nursing guidelines. Overall, thirty-six studies (68%) reported a positive significant effect of the implementation of guidelines on patient-related nursing outcomes or guideline adherence. Future studies should use a standardized reporting checklist to ensure a detailed description of the used implementation strategies to increase reproducibility and understanding of outcomes.
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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 | 0.047 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".