Outcomes‐Focused Knowledge Translation: A Framework for Knowledge Translation and Patient Outcomes Improvement
Bibliographic record
Abstract
BACKGROUND: Regularly accessing information that is current and reliable continues to be a challenge for front-line staff nurses. Reconceptualizing how nurses access information and designing appropriate decision support systems to facilitate timely access to information may be important for increasing research utilization. DESCRIPTION OF STRATEGY: An outcomes-focused knowledge translation framework was developed to guide the continuous improvement of patient care through the uptake of research evidence and feedback data about patient outcomes. The framework operationalizes the three elements of the PARIHS framework at the point of care. Outcomes-focused knowledge translation involves four components: (a) patient outcomes measurement and real-time feedback about outcomes achievement; (b) best-practice guidelines, embedded in decision support tools that deliver key messages in response to patient assessment data; (c) clarification of patients' preferences for care; and (d) facilitation by advanced practice nurses and practice leaders. In this paper the framework is described and evidence is provided to support theorized relationships among the concepts in the framework. IMPLICATIONS: The framework guided the design of a knowledge translation intervention aimed at continuous improvement of patient care and evidence-based practice, which are fostered through real-time feedback data about patient outcomes, electronic access to evidence-based resources at the point of care, and facilitation by advanced practice nurses. The propositions in the framework need to be empirically tested through future research.
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 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 | 0.148 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".