Effectiveness of interventions involving nurses in secondary stroke prevention: A systematic review and meta-analysis
Bibliographic record
Abstract
Background and purpose: Among members of the health care team, nurses play a large role in actively engaging stroke survivors in secondary stroke prevention programs. This systematic review and meta-analysis examines the effectiveness of interventions in which nurses have a primary role on modification of risk factors among stroke survivors. Methods: We systematically searched for randomized controlled trials in relevant databases investigating the role of nurses in secondary stroke prevention. Meta-analyses were conducted using Cochrane Review Manager Software. The mean pooled effect size, a 95% confidence interval (CI), and I-squared ( I2) for heterogeneity were calculated. Results: Sixteen randomized controlled trials were included with a total of 3568 stroke and transient ischemic attack patients. After removing one outlier, the models demonstrated a statistically significant effect on reducing systolic blood pressure (SMD = −0.14 (95% CI = −0.23, –0.05), I2 = 0%; p = 0.002, six studies, n =1885) and diastolic blood pressure (SMD = −0.16 (95% CI = −0.27, –0.05), I2 = 0%; p = 0.003, four studies, n =1316). The interventions also significantly improved physical activity (five studies, n=1234), diet (three studies, n=425), medication adherence (two studies, n=270), and knowledge of risk factors (three studies, n=516). However, there was no effect on smoking cessation or reduction in use of alcohol. Conclusion: We found that interventions in which nurses had a primary role were effective on improving medical and behavioral risk factors, as well as knowledge of risk factors as part of secondary prevention of stroke.
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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.048 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".