Changing Nursing Practice: Evaluating the Usefulness of a Best-Practice Guideline Implementation Toolkit
Bibliographic record
Abstract
PURPOSE: This pilot study describes the evaluation of an 88-page Toolkit that was developed to guide nursing leaders, including advanced practice nurses, managers and steering committees, who were responsible for coordinating implementation of selected best-practice guidelines (BPG) in their respective agencies. METHODS: The self-administered questionnaire was mailed to all clinical resource nurses and steering committee members involved in implementing best-practice guidelines. The questionnaire evaluated the usefulness of the content of five chapters (and the case scenarios and worksheets included with each chapter). RESULTS: Sixty-eight percent of respondents returned the questionnaire. More than 85% of them found the Toolkit helpful during the implementation process; 83% reported using it, 80% said they would use it again. The Toolkit was used primarily to identify, analyze and engage stakehoLders, and to assess environmental readiness. Fifty-seven percent of respondents said they used the Toolkit to plan the implementation strategy. CONCLUSIONS: The Toolkit assessed in this evaluation shows promise as a useful guide for those charged with BPG implementation. Like other guidelines that are based on evidence, the Toolkit will require occasional updates to ensure that the strategies proposed reflect current evidence. Nursing leaders have a responsibility to keep up to date and to provide efficient and effective healthcare services. Best-practice guidelines or clinical practice guidelines are useful tools that synthesize the latest evidence and provide recommendations for care providers aiming to improve the quality of patient care (Grol 2001). Many leaders are challenged to know how and when to implement the increasing numbers of practice guidelines. The purpose of this article is to describe a pilot study to evaluate a Toolkit that was developed to guide nursing leaders in implementing selected best-practice guidelines (BPGs) in their respective agencies.
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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 | 0.140 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".