Preparing Nursing Contexts for Evidence‐Based Practice Implementation: Where Should We Go From Here?
Bibliographic record
Abstract
BACKGROUND: Context is important to the adoption and sustainability of evidence-based practices (EBPs). Currently, most published implementation efforts address context in relation to one specific EBP or a bundle of related EBPs. Since EBP and implementation are ongoing and dynamic, more discussion is needed on preparing nursing contexts to be more conducive to implementation generally. AIM: To discuss the need to create contexts that are more adaptable to ongoing change due to the dynamic nature of EBPs and the ever-changing healthcare environment. METHODS: This paper builds on a collection of our previous work, as nursing implementation scientists representing the Canadian and American healthcare contexts, and a literature review of the implementation science, knowledge translation, and sustainability literatures from 2006 to 2019. RESULTS: We argue for a different way of thinking about the influence of context and implementation of EBPs. We contend that nursing contexts must be prepared to be more flexible and conducive to ongoing EBP implementation more generally. Contexts that embrace, facilitate, and have the capacity for change may be more likely to effectively de-implement ineffective interventions or implement and sustain new EBPs. We outline future directions to build a program of research on preparing the soil for implementation of EBPs, including building capacity among nurses, supporting organizations to embrace change, co-producing research evidence, and contributing to implementation science. LINKING EVIDENCE TO ACTION: Supporting contexts to adopt and sustain evidence in nursing practice is essential for bridging the evidence to practice gap and improving outcomes for patients, clinicians, and the health system. Moving forward, we need to develop a better understanding of how to create contexts that embrace change prior to the implementation of EBPs in order sustain improvements to patient and health system 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 | 0.200 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.045 | 0.075 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.025 | 0.039 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".