Indicators to measure implementation and sustainability of nursing best practice guidelines: A mixed methods analysis
Bibliographic record
Abstract
Background: The use of best practice guidelines (BPGs) has the potential to decrease the gap between best evidence and nursing and healthcare practices. We conducted an exploratory mixed method study to identify strategies, processes, and indicators relevant to the implementation and sustainability of two Registered Nurses' Association of Ontario (RNAO) BPGs at Best Practice Spotlight Organizations® (BPSOs). Methods: Our study had four phases. In Phase 1, we triangulated two qualitative studies: a) secondary analysis of 126 narrative reports detailing implementation progress from 21 BPSOs spanning four sectors to identify strategies and processes used to support the implementation and sustainability of BPGs and b) interviews with 25 guideline implementers to identify additional strategies and processes. In Phase 2, we evaluated correlations between strategies and processes identified from the narrative reports and one process and one outcome indicator for each of the guideline. In Phase 3, the results from Phases 1 and 2 informed indicator development, led by an expert panel. In Phase 4, the indicators were assessed internally by RNAO staff and externally by Ontario Health Teams. A survey was used to validate proposed indicators to determine relevance, feasibility, readability, and usability with knowledge users and BPSO leaders. Results: Triangulation of the two qualitative studies revealed 46 codes of implementation and sustainability of BPGs, classified into eight overarching themes: Stakeholder Engagement, Practice Interventions, Capacity Building, Evidence-Based Culture, Leadership, Evaluation & Monitoring, Communication, and Governance. A total of 28 structure, process, or outcome indicators were developed. End users and BPSO leaders were agreeable with the indicators according to the validation survey. Conclusions: Many processes and strategies can influence the implementation and sustainability of BPGs at BPSOs. We have developed indicators that can help BPSOs promote evidence-informed practice implementation of BPGs.
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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 | 0.297 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.026 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".