Stroke Preparedness in Children: Translating Knowledge into Behavioral Intent: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: If translated into behavioral intent, improving stroke knowledge may potentially impact on better outcomes. Children are an attractive target population since they can drive familial behavioral changes. However, the impact of interventions on stroke knowledge among children is unclear. We performed a systematic review and meta-analysis to investigate whether educational interventions targeting children improve stroke knowledge and lead to behavioral changes. METHODS: We searched Ovid, PubMed, and Embase between January 2000 and December 2014. We included studies written in English reporting the number of children aged 6-15 years undergoing educational interventions on stroke and providing the results for baseline and early and late postintervention tests. We compared the proportion of correct answers between baseline, early, and late responses for two endpoints: knowledge and behavioral intent. RESULTS: Of the initial 58 articles found, we included nine that met the inclusion criteria. Compared with baseline tests (51·7%, 95% confidence interval 40·9-62·4), there was improvement in stroke knowledge in early (74·0%, 95% confidence interval 64·4-82·5, P = 0·002) and late (67·3%, 95% confidence interval 55·4-78·2, P = 0·027) responses. There was improvement in the early (92·1%, 95% confidence interval 86·0-96·6, P < 0·001) and late (83·9%, 95% confidence interval 73·5-92·1, P = 0·001) responses for behavioral intent compared with the baseline assessment (63·8%, 95% confidence interval 53·5-73·4). CONCLUSION: Children are a potentially attractive target population for improvement in stroke knowledge and behavioral intent, both in the short and long term. Our findings may support the implementation of large-scale stroke educational initiatives targeting children.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".