Effectiveness of public stroke educational interventions: a review
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recognizing stroke symptoms and acting quickly can reduce death and disability, but public awareness of stroke risk factors, symptoms and what to do about them is still limited. Stroke educational campaigns are used worldwide but there are few published evaluations of such campaigns. METHODS: The literature from 1999 to 2012 on the effectiveness of stroke educational campaigns was reviewed and summarized with narrative synthesis. Web-based campaigns were also described. Three databases and one search engine were explored with two keywords (stroke campaign and stroke promotion). The reference lists of all included articles were also examined. RESULTS: Twenty-two intervention studies and five web-based campaigns were included in the review. Most interventions proved partially effective, in terms of gender preference (women) or type of information retained or media preferred. Only one intervention proved ineffective. Mass media campaigns can be effective but require sustained funding, and their ability to target high-risk subgroups, whether aging, linguistic or socioeconomic, is unclear. Three community-based participatory stroke promotion interventions proved partially effective, but the small sample sizes might have underpowered the results. Web-based campaigns are efficient in reaching a large number of people but tend to attract a selected and self-selected population. CONCLUSIONS: Stroke educational campaigns have the potential to improve knowledge and awareness and change the behavior of a large number of people. Health promoters and investigators must adopt flexibility and participatory mentality to develop cost-effective interventions. Both community-based campaigns and E-tools should be integrated within a comprehensive multifaceted stroke promotion strategy to expand their reach.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".