P207 Toolkit: Implementation Of Best Practice Guidelines – A Framework For Success!
Bibliographic record
Abstract
A signature programme lead by a professional nursing association in Canada has a mandate to develop, disseminate, implement, evaluate and support the uptake of clinical guidelines. This programme’s success has resulted in international reach, due to its rigorous guideline development process, and innovative implementation strategies. These strategies include a key resource, the Toolkit: Implementation of Best Practice Guidelines, which delineates a systematic, well-planned implementation process, and is designed to assist nurses and other health-care professionals to support evidence-informed clinical decision-making. This Toolkit is based on emerging evidence that the likelihood of achieving successful uptake of best practice in health care increases when: • Guidelines are selected for implementation through systematic, participatory processes including relevant stakeholder engagement and environmental readiness assessment • Guidelines are tailored to the local context • Barriers and facilitators to guideline use are assessed and addressed • Guideline use is systematically monitored and sustained • Evaluation of the impacts of guideline use is an integral part of the entire process • There are adequate resources to support completion of all aspects of implementation This Toolkit will help guideline users take best evidence and integrate it into practice, education and policy using a systematic approach consistent with the local context of practice. This presentation will share the key phases of guideline implementation outlined in the Toolkit, and discuss how this resource is being utilised to address the key challenges of developing evidence based practice cultures through guideline 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 | 0.155 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.009 | 0.031 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.015 |
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".