Increasing research use in nursing: implications for clinical educators and managers
Bibliographic record
Abstract
Increasing use of research in health care is a priority for stakeholders worldwide. National funding agencies such as the Canadian Health Services Research Foundation1 and the Canadian Institutes for Health Research2 have dedicated substantial resources to meeting this goal. Professional organisations3 ,4 actively encourage their members to integrate findings into practice. Similarly, the UK and USA have initiated comparable initiatives.5–7 This movement stems from a belief that incorporating research findings in clinical practice can improve patient outcomes while making health care more efficient. However, despite the many initiatives dedicated to implementing research findings in clinical practice, their success remains limited.8–10 There is little evidence-based guidance on how to improve research use in clinical practice.11 ,12 Modest effects are achieved, for example, with audit and feedback, reminders, and educational outreach, but results are often inconsistent10 and difficult to extrapolate to real-world settings. Recently, organisations, rather than individual practitioners, have been a focus of research uptake. However, in an overview of organisational interventions, Wensing et al found that, similar to interventions directed towards individuals (eg, audit and feedback, reminders), no organisational intervention is consistently effective.13 Overall, there is limited empirical guidance for those interested in increasing research use in clinical practice. In this Notebook, we summarise the findings of a systematic review of the effectiveness of interventions aimed at increasing research use in nursing14 and provide recommendations for nurse managers and educators based on current evidence. Our recommendations draw on 4 related bodies of literature: (1) evidence-based practice implementation strategies in health care (primarily medicine); (2) nurses’ sources of knowledge; (3) organisational characteristics; and (4) organisational boundaries. We conclude by briefly discussing 2 strategies that show promise: audit and feedback and local opinion leaders. The systematic review …
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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 | 0.187 | 0.490 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.019 | 0.043 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 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".