An evaluation study of the implementation of stroke best practice guidelines using a Knowledge Transfer Team approach.
Bibliographic record
Abstract
INTRODUCTION: Strokes will become an increasing burden on the Canadian health care and social systems in coming years. Caring for people who have experienced a stroke is a challenging issue. The Registered Nurses Association of Ontario (RNAO) developed Stroke Assessment Across the Continuum Best Practice Guidelines (BPGs) to support the best possible care for this population. This article reports the findings of an evaluation of the implementation of recommendations from the stroke BPGs using a Knowledge Transfer Team (KTT) at Mackenzie Health's Integrated Stroke Unit in Richmond Hill, Ontario. METHODS: Over a 12-month period, an evaluation of the implementation activities using structure, process, and outcome indicators, as well as identifying effective strategies for system-wide dissemination of BPG implementation and outcomes was completed. Data were collected from the staff, KTT members, and patients and their providers. RESULTS: The results clearly illustrate that all of the health care professionals involved in the study felt the KT approach was an effective method of implementing and disseminating the stroke BPGs. The main limitations perceived by staff and KTT members were time constraints, difficulty recruiting a larger sample size, competing priorities, lack of compliance, changes to charting, staff attrition, and a lack of financial support. CONCLUSION: The KTT approach resulted in improved patient care and outcomes, as illustrated by the high patient satisfaction levels.
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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 | 0.053 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".