Use and effects of implementation strategies for practice guidelines in nursing: a systematic review
Bibliographic record
Abstract
BACKGROUND: Practice guidelines can reduce variations in nursing practice and improve patient care. However, implementation of guidelines is complex and inconsistent in practice. It is unclear which strategies are effective at implementing guidelines in nursing. This review aimed to describe the use and effects of implementation strategies to facilitate the uptake of guidelines focused on nursing care. METHODS: We conducted a systematic review of five electronic databases in addition to the Cochrane Effective Practice and Organization of Care (EPOC) Group specialized registry. Studies were included if implementation of a practice guideline in nursing and process or outcome of care provided by nurses were reported. Two reviewers independently screened studies, assessed study quality, extracted data, and coded data using the EPOC taxonomy of implementation strategies. For those strategies not included in the EPOC taxonomy, we inductively categorized these strategies and generated additional categories. We conducted a narrative synthesis to analyze results. RESULTS: The search identified 46 papers reporting on 41 studies. Thirty-six studies used a combination of educational materials and educational meetings. Review findings show that multicomponent implementation strategies that include educational meetings, in combination with other educational strategies, report positive effects on professional practice outcomes, professional knowledge outcomes, patient health status outcomes, and resource use/expenditures. Twenty-three of the 41 studies employed implementation strategies not listed within the EPOC taxonomy, including adaptation of practice guidelines to local context (n = 9), external facilitation (n = 14), and changes to organizational policy (n = 3). These implementation strategies also corresponded with positive trends in patient, provider, and health system outcomes. CONCLUSIONS: Nursing guideline implementation may benefit from using the identified implementation strategies described in this review, including participatory approaches such as facilitation, adaptation of guidelines, and organizational policy changes. Further research is needed to understand how different implementation strategy components work in a nursing context and to what effect. As the field is still emerging, future reviews should also explore guideline implementation strategies in nursing in quasi or non-experimental research designs and qualitative research studies.
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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 | 0.050 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".