Nursing unit leaders' influence on the long-term sustainability of evidence-based practice improvements
Bibliographic record
Abstract
AIM: To describe how actions of nursing unit leaders influenced the long-term sustainability of a best practice guidelines (BPG) program on inpatient units. BACKGROUND: Several factors influence the initial implementation of evidence-based practice improvements in nursing, with leadership recognized as essential. However, there is limited knowledge about enduring change, including how frontline nursing leaders influence the sustainability of practice improvements over the long term. METHODS: A qualitative descriptive case study included 39 in-depth interviews, observations, and document reviews. Four embedded nursing unit subcases had differing levels of program sustainability at 7 years (average) following implementation. RESULTS: Higher levels of BPG sustainability occurred on units where formal leadership teams used an integrated set of strategies and activities. Two key strategies were maintaining priorities and reinforcing expectations. The coordinated use of six activities (e.g., discussing, evaluating, integrating) promoted the continuation of BPG practices among staff. These leadership processes, fostering exchange and learning, contributed to sustainability-promoting environments characterized by teamwork and accountability. CONCLUSIONS: Unit leaders are required to strategically orchestrate several overlapping and synergistic efforts to achieve long-term sustainability of BPG-based practice improvements. IMPLICATIONS: As part of managing overall unit performance, unit leaders may influence practice improvement sustainability by aligning vision, strategies, and activities.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".