P153 Successful Strategies In The Implementation Of Clinical Practice Guidelines: Creating Evidence-Informed Practice Cultures
Bibliographic record
Abstract
For over a decade, a professional nursing association has lead a programme focused on the development, dissemination, implementation and evaluation of clinical and healthy work environment guidelines. This programme has achieved considerable recognition around the globe based on its rigorous guideline development process, and innovative implementation strategies. These strategies are founded on an evidence-informed implementation model, and include individual, organisational and system level approaches. A key organisational level strategy within this programme, the Best Practice Spotlight Organization (BPSO) initiative, was designed to support health care organisations in their journey towards clinical excellence through the implementation and sustainability of multiple clinical practice guidelines. This initiative was launched in 2003 with nine organisations (acute care hospitals and home health care agencies), and has grown to include 68 BPSOs representing 294 sites. This reach has included international BPSOs in Spain, Australia, Chile, and the United States. The BPSO initiative provides specific coaching, mentoring, knowledge transfer and capacity building opportunities, and support to leaders in the BPSO sites as they implement, evaluate and work to sustain clinical guidelines both across their organisations and at the team/unit level. This strategic approach has served to trigger the development of evidence informed cultures, improve patient care and enrich the professional practice of nurses and other health care providers. This presentation will share some of the key outcomes of this guideline implementation strategy, and will highlight success storeys of how BPSOs are changing the nursing and health care landscape to foster a culture of evidence informed practice.
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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 | 0.217 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".