A Multisite Health System Survey to Assess Organizational Context to Support Evidence‐Based Practice
Bibliographic record
Abstract
BACKGROUND: Implementation and sustainability of a culture of evidence-based practice (EBP) require a systematic approach. A baseline assessment of the organizational context can inform implementation efforts. AIMS: To examine organizational hospital context and provider characteristics associated with EBP readiness and to describe EBP context across hospitals. METHODS: A nonexperimental descriptive correlational design was used to conduct a web-based survey of direct-care registered nurses (N = 701) and nurse managers (N = 94) across a large Midwestern multisite healthcare system using the Alberta Context Tool (ACT). RESULTS: Many significant relationships existed among nurse characteristics and ACT domains, including age (lower age had higher Leadership, Evaluation, and Formal Interactions), education (graduate education had lower Social Capital than a bachelor's or associate degree), role (direct-care nurses had lower Culture than managers and lower Social Capital), and work status (full-time employees had lower Evaluation and Social Capital). EBP context across type of hospitals is similar, with marginal differences in Social Capital and Organizational Slack (higher in critical access hospitals). LINKING EVIDENCE TO ACTION: Assessing organizational context to support EBP is the first step in developing and enhancing a sustainable culture of inquiry. The ACT has been tested across countries, settings, and healthcare disciplines to measure perception of readiness of the practice environment toward EBP. Optimal organizational context is essential to support EBP and sustain the use of evidence in professional nursing practice. Nursing leaders can use baseline assessment information to identify strengths and opportunities to enhance EBP implementation. Enhancing organizational context across nurse characteristics (e.g., age, role, and work status) to acknowledge nurses' contributions, balance nurses' personal and work life, enhance connectedness, and support work culture is beneficial. Fostering development of Social Capital in nurses is needed to influence EBP readiness. A systematic and standardized approach to foster EBP across health systems is key to successful implementation.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".