Using theory and evidence to drive measurement of patient, nurse and organizational outcomes of professional nursing practice
Bibliographic record
Abstract
An evolving body of literature suggests that the implementation of evidence based clinical and professional guidelines and strategies can improve patient care. However, gaps exist in our understanding of the effect of implementation of guidelines on outcomes, particularly patient outcomes. To address this gap, a measurement framework was developed to assess the impact of an organization-wide implementation of two nursing-centric best-practice guidelines on patient, nurse and organizational level outcomes. From an implementation standpoint, we anticipate that our data will show improvements in the following: (i) patient satisfaction scores and safety outcomes; (ii) nurses ability to value and engage in evidence based practice; and (iii) organizational support for evidence-informed nursing care that results in quality patient outcomes. Our measurement framework and multifaceted methodological approach outlined in this paper might serve as a blueprint for other organizations in their efforts to evaluate the impacts associated with implementation of clinical and professional guidelines and best practices.
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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 | 0.225 | 0.464 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.030 | 0.017 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".