Knowledge Acquisition and Audience Retention in Stroke Education: Results From a Global Study by the World Stroke Organization
Bibliographic record
Abstract
BACKGROUND: The World Stroke Academy (WSA), the educational platform of the WSO, provides high-quality stroke education to health care professionals worldwide. Understanding the learning needs and preferences of WSA members is crucial for effective knowledge translation. This study aims to (1) assess demographics and professional backgrounds of WSA members, (2) identify preferences in knowledge acquisition, and (3) evaluate audience retention during WSA webinars. METHODS: A survey was developed using Qualtrics and distributed via email to all WSA members from May 8 to May 23, 2023. The survey included multiple-choice, rating scale, and ranking questions. Audience retention data were obtained from the latest 6 WSA webinars (May 2023–December 2023). Descriptive variables were reported, and χ 2 analysis and multinomial regression models were used. RESULTS: A random sample of 1065 WSO members received the survey; 327 initiated it (participation rate, 30.7%), and 236 completed it (completion rate, 72.2%). The mean age (SD) of participants was 46.7 (±11.6) years; 57.2% identified as male. Most respondents were stroke specialist physicians (65.3%) and were based in Europe (35.2%). Online journal articles represented 41.6% of the total time allocated for stroke education, with 17% allocated to webinars. Social media usage patterns showed X (formerly Twitter) as the top choice (34.7%). Age, profession, and location significantly influenced social media platform use. Audience retention was at 50% at the 57-minute mark and 44.3% at the end of webinars. CONCLUSIONS: Tailoring WSA webinar content to meet health care professionals’ needs and enhancing interactive components can improve audience retention. These insights will guide the future development of the WSA portfolio.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".