Knowledge Translation in Nursing Through Decision Support at the Point of Care
Bibliographic record
Abstract
With advances in electronic health record systems and mobile computing technologies it is possible to re-conceptualize how health professionals access information and design appropriate decision-support systems to support quality patient care. This chapter uses the context of nursing-sensitive patient outcomes data collection to explore how technology can be used to increase nurses’ and other health professionals’ access to patient outcomes information in real time to continually improve patient care. The chapter draws upon literature related to: (1) case-based reasoning, (2) feedback, (3) and evidence-based nursing practice to provide the theoretical foundation for an electronic knowledge translation intervention that was developed and tested for usability. Directions for future research include the need to understand how nurses experience uncertainty in their practice, how this influences information seeking behavior, and how information resources can be designed to support real-time clinical decision making.Request access from your librarian to read this chapter's full text.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".