Organisational contextual drivers of evidence-based practice across acute and primary care
Bibliographic record
Abstract
BACKGROUND: Evidence based practice (EBP) is widely recognised as fundamental to high quality nursing care, yet implementation remains uneven across healthcare settings in England. Attention has shifted from individual barriers to organisational context. Leadership, team dynamics, access to resources, and social capital shape how nurses engage with EBP. Despite national policies promoting research active environments, how these ambitions are realised at the frontline is unclear. This study examined how organisational factors influence nurses’ implementation of evidence across acute and primary care. METHODS: A cross-sectional design was used with registered nurses working in acute and primary care settings. Two validated instruments, the Evidence Based Practice Implementation Scale and the Alberta Context Tool, were administered. A nonprobability sampling strategy targeted the acute and general practice nursing workforce. Response distributions were monitored across pre specified strata and fieldwork closed once coverage and precision criteria were met. Descriptive statistics summarised participant and organisational characteristics. Inferential analyses compared settings, mediation modelling tested the role of social capital in the leadership to EBP pathway, and cluster analysis identified implementation profiles. RESULTS: Engagement with EBP was moderate overall (M = 3.16, SD = 0.88) with no significant difference between sectors (p = 0.38). Acute care nurses reported higher leadership support (M = 4.01 versus 3.78, p = 0.008) and better access to structural resources (M = 3.35 vs. 3.10, p = 0.004). Within acute care, leadership differed across specialties, with higher scores in ICU or CCU and general medicine, F (4, 636) = 4.12, p = 0.003. Social capital significantly mediated the association between leadership and EBP implementation (β = 0.15, 95% CI 0.10–0.21). Three engagement clusters were identified, high 32%, moderate 45%, and low 23%, each with distinct organisational profiles. CONCLUSION: Organisational context, particularly leadership and social capital, is central to nurses’ capacity to implement evidence. Variation across specialties and sectors indicates that a one size fits all approach is unlikely to succeed. Policy relevant levers include formalising protected time, resourcing embedded facilitation, investing in knowledge infrastructure, and expanding clinical academic pathways, to create environments where evidence use is routine and supported. CLINICAL TRIAL NUMBER: Not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".