Best Practice Guideline Development Methods: An Integrated Approach Based on the Registered Nurses' Association of Ontario's Methods
Bibliographic record
Abstract
ABSTRACT Since 1999, the Registered Nurses' Association of Ontario (RNAO) has developed best practice guidelines (BPG) to promote consistency and quality of evidence‐based care and improve patient, organization and health system outcomes. A distinguishing feature of RNAO's BPG development portfolio is its integration within RNAO's three‐pillar BPG programme. This programme supports health service and academic organizations, Best Practice Spotlight Organizations® (BPSO®), to systematically implement, monitor and evaluate BPGs. Through formal partnerships with these organizations, the BPSO network extends the reach of the BPG programme and provides RNAO with direct feedback from end users, strengthening the relevance of future guideline editions. This article describes RNAO's integrated BPG development methods, outlining how evidence‐based recommendations are made to support organizations implementing BPGs. Following extensive pre‐development work to shape the purpose and scope of each guideline, an interprofessional expert panel that includes people with lived experience is appointed. Using the Grading of Recommendations, Assessment, Development and Evaluation methods, the expert panel prioritizes research questions and systematic reviews are conducted to determine strong or conditional recommendations. Good practice statements are also included and resources to support guideline implementation and evaluation are formulated including fact sheets, RNAO Clinical Pathways™ and evaluation measures. Draft guidelines undergo external review to ensure relevance and usability. To best serve the needs of organizations implementing BPGs, ongoing efforts are being made to keep RNAO's methods up‐to‐date and incorporate evaluation data from implementing organizations into the guideline development process.
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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 | 0.083 | 0.308 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads 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".