Managerial Leadership for Nurses' Use of Research Evidence: An Integrative Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Integration of research evidence into clinical nursing practice is essential for the delivery of high-quality nursing care. Leadership behaviours of nurse managers and administrators have been identified as important to support research use and evidence-based practice. Yet minimal evidence exists indicating what constitutes effective nursing leadership for this purpose, or what kinds of interventions help leaders to successfully influence research-based care. AIMS: (1) To describe leadership activities of nurse managers that influence nurses' use of research evidence; and (2) to identify interventions aimed at supporting nurse managers to influence research use in clinical nursing practice. METHODS: A search of electronic databases was conducted for studies on behaviours or activities of nurse managers/administrators and the use of research evidence by nurses. Sifting, screening, and quality assessments were done by two reviewers. Results were synthesized by study type (quantitative and qualitative) and reported. RESULTS: Twelve studies met inclusion criteria (eight quantitative, four qualitative). Three activities were found in quantitative studies that influenced nurses' use of research: managerial support, policy revisions, and auditing. Qualitative studies showed organizational issues as barriers to managers' abilities to affect research use, while role modeling and valuing research facilitated research use. Four studies, one of which was experimental, included an intervention to support managers, but all had insufficient information about leadership development. CONCLUSIONS: To date, important descriptive work highlights the strategic role managers have in research transfer. Both facilitative and regulatory activities appear to be necessary for managers to influence research use. These findings have important implications for evolving theoretical models describing factors that affect the process of research utilization. It is time to move the science forward and test a hypothesis linking leadership to outcomes. Qualitative methods are essential for understanding the process of leadership for research transfer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 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".