[Implementation of the "Best Practice Spotlight Organization" Program at the Virgen de las Nieves University Hospital.]
Bibliographic record
Abstract
) program in the cohort (2015-2017) to implement three guidelines for Nurses Association of Canada Ontario (RNAO) clinical practice of care. The methodology used was the model called "knowledge for action" and the actions developed for each of the phases of the action cycle for applying knowledge to practice were described: 1) identification of the problem, 2) adaptation to the local context, 3) evaluation of facilitators and barriers, 4) adaptation and implementation of interventions, 5) monitoring and evaluation of results and 6) sustainability. This work adds to the set of studies that address the improvement and maintenance of evidence-based practice programs in nursing, and in health services in general. It shows the application of a framework for the implementation of clinical practice guidelines for care in a specific health environment for its replication in other different health settings. It has been shown that it is essential to dedicate efforts to planning the implementation of this type of programs, taking into account the context in which they are developed, the specific characteristics of the population being served, identifying the different barriers and facilitators that may affect during the course of the program. process and defining actions to make the changes in practice sustainable.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".