Interventions aimed at increasing research use in nursing: a systematic review
Bibliographic record
Abstract
BACKGROUND: There has been considerable interest recently in developing and evaluating interventions to increase research use by clinicians. However, most work has focused on medical practices; and nursing is not well represented in existing systematic reviews. The purpose of this article is to report findings from a systematic review of interventions aimed at increasing research use in nursing. OBJECTIVE: To assess the evidence on interventions aimed at increasing research use in nursing. METHODS: A systematic review of research use in nursing was conducted using databases (Medline, CINAHL, Healthstar, ERIC, Cochrane Central Register of Controlled Trials, and Psychinfo), grey literature, ancestry searching (Cochrane Database of Systematic Reviews), key informants, and manual searching of journals. Randomized controlled trials and controlled before- and after-studies were included if they included nurses, if the intervention was explicitly aimed at increasing research use or evidence-based practice, and if there was an explicit outcome to research use. Methodological quality was assessed using pre-existing tools. Data on interventions and outcomes were extracted and categorized using a pre-established taxonomy. RESULTS: Over 8,000 titles were screened. Three randomized controlled trials and one controlled before- and after-study met the inclusion criteria. The methodological quality of included studies was generally low. Three investigators evaluated single interventions. The most common intervention was education. Investigators measured research use using a combination of surveys (three studies) and compliance with guidelines (one study). Researcher-led educational meetings were ineffective in two studies. Educational meetings led by a local opinion leader (one study) and the formation of multidisciplinary committees (one study) were both effective at increasing research use. CONCLUSION: Little is known about how to increase research use in nursing, and the evidence to support or refute specific interventions is inconclusive. To advance the field, we recommend that investigators: (1) use theoretically informed interventions to increase research use, (2) measure research use longitudinally using theoretically informed and psychometrically sound measures of research use, as well as, measuring patient outcomes relevant to the intervention, and (3) use more robust and methodologically sound study designs to evaluate interventions. If investigators aim to establish a link between using research and improved patient outcomes they must first identify those interventions that are effective at increasing research use.
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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 | 0.036 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".