Establishing and Sustaining a Culture of Evidence-Based Practice: An Evaluation of Barriers and Facilitators to Implementing the Best Practice Spotlight Organization Program in the Australian Healthcare Context
Bibliographic record
Abstract
BACKGROUND: Nurses and midwives are central to the implementation and delivery of quality care through evidence-based practice (EBP). However, implementation of EBP in nursing and midwifery is under-researched with few examples of systematic and sustained change. The Registered Nurses Association of Ontario's Best-Practice Spotlight Organization (BPSO) Program was adopted in South Australia as a framework to systematically implement EBP in two diverse and complex healthcare settings. METHODS: The study was a post-implementation, mixed-method evaluation conducted at two healthcare settings in Adelaide, South Australia utilizing qualitative and quantitative data. Proctor's implementation evaluation framework guided the evaluation design. Information sources included; interviews, focus groups, questionnaires, and document review. RESULTS: = 109 participants) from a broad range of stakeholder groups participated in the interviews, focus groups, and returned questionnaires. A number of facilitators directly affecting program implementation were identified; these pertained to embedding continuity into the program's implementation and delivery, a robust governance structure, and executive sponsorship. Barriers to implementation were also identified. These barriers pertained to organizational or workforce challenges; staff turnover and movement (e.g., secondment), insufficient staff to allow people to attend training, and a lack of organizational commitment to the program, especially at an executive level. As a result of successful implementation, it was observed that over three years, the BPSO program positively influenced the uptake and implementation of EBP by clinicians and the organizations into which they were introduced. CONCLUSIONS: The BPSO model can be translocated to new healthcare systems and has the potential to act as a mechanism for establishing and sustaining EBP change. This study was the first to apply an implementation evaluation framework to the BPSO program, which allowed for structured analysis of facilitating or impeding factors that affected implementation success. The findings have important implications for other health systems looking to translocate the same or similar EBP programs, as well as contributing to the growing body of implementation evaluation literature.
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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 | 0.152 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 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".