Getting guidelines into practice
Bibliographic record
Abstract
BACKGROUND: The Spanish Best Practice Guidelines (BPG) Implementation Project is part of the Best Practice Spotlight Organizations international program, coordinated by the Registered Nurses' Association of Ontario (RNAO). AIMS: To influence the uptake of nursing BPG across healthcare organizations, to enable practice excellence and positive client outcomes. METHODS: After translating the RNAO's BPG into Spanish, the Host Organization published a formal call for proposals to select healthcare settings in Spain to implement the RNAO's BPG and evaluate the results. The approach is nursing-led and multidisciplinary; context specific; and involving a wide range of stakeholders. The implementation of BPG Toolkit guides the process: cascade training, selection of recommendations to be implemented, 3 years of planned implementation activities, monitoring of process and outcome results for patients discharged 60 days every year. The Host Organization supports healthcare settings selected. RESULTS/DISCUSSION: The first call was launched in 2012. Eight healthcare settings (11 sites), serving 1.3 million people, were selected (hospitals and primary healthcare centers). They chose 10 BPG, according to their needs. In 2015 and 2018, 16 more healthcare settings have joined the program with a total of 263 sites. And in 2019, three complete regions will join the program as a regional host. Currently, more than 3200 nurses and 40 other healthcare professionals have been trained, evidence-based protocols have been developed or updated, patient education has been promoted, and international Best Practice Spotlight Organizations indicators have been evaluated in an electronic platform. CONCLUSION: The results obtained acknowledge that the RNAO implementation method could be replicated with success internationally. The strategies based on local context have worked and we have consolidated a network that shares knowledge and strategies and promotes evidence-based culture among Spanish healthcare settings and evidence-based care to patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".