Leadership for Evidence‐Based Practice: Strategic and Functional Behaviors for Institutionalizing EBP
Bibliographic record
Abstract
BACKGROUND: Making evidence-based practice (EBP) a reality throughout an organization is a challenging goal in healthcare services. Leadership has been recognized as a critical element in that process. However, little is known about the exact role and function of various levels of leadership in the successful institutionalization of EBP within an organization. AIMS: To uncover what leaders at different levels and in different roles actually do, and what actions they take to develop, enhance, and sustain EBP as the norm. METHODS: Qualitative data from a case study regarding institutionalization of EBP in two contrasting cases (Role Model and Beginner hospitals) were systematically analyzed. Data were obtained from multiple interviews of leaders, both formal and informal, and from staff nurse focus groups. A deductive coding schema, based on concepts of functional leadership, was developed for this in-depth analysis. RESULTS: Participants' descriptions reflected a hierarchical array of strategic, functional, and cross-cutting behaviors. Within these macrolevel "themes," 10 behavioral midlevel themes were identified; for example, Intervening and Role modeling. Each theme is distinctive, yet various themes and their subthemes were interrelated and synergistic. These behaviors and their interrelationships were conceptualized in the framework "Leadership Behaviors Supportive of EBP Institutionalization" (L-EBP). Leaders at multiple levels in the Role Model case, both formal and informal, engaged in most of these behaviors. LINKING EVIDENCE TO ACTION: Supportive leadership behaviors required for organizational institutionalization of EBP reflect a complex set of interactive, multifaceted EBP-focused actions carried out by leaders from the chief nursing officer to staff nurses. A related framework such as L-EBP may provide concrete guidance needed to underpin the often-noted but abstract finding that leaders should "support" EBP.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".