The World Stroke Academy: A World Stroke Organization global pathway to improve knowledge in stroke care
Bibliographic record
Abstract
BACKGROUND: The World Stroke Academy (WSA) (www.world-stroke-academy.org) is the educational platform of the World Stroke Organization (WSO). It facilitates educational activities (e.g. webinars and eLearning modules) and supports the WSO mission by providing high-quality stroke education to healthcare professionals. It provides evidence-based educational materials in a variety of formats to meet the needs of the WSO membership. AIM: This article introduces the WSA, its core activities, and outlines how to access the many educational resources it offers. RESULTS: The WSA offers high-quality peer reviewed stroke education material and uses outcome metrics to assess and improve the quality of medical training of healthcare professionals. This article also highlights the importance of identifying knowledge and knowledge-to-action gaps through the creation of special projects and initiatives. It describes three areas in which the WSA has carried out recent educational initiatives, namely: life after stroke, women in stroke, and stroke checklist/pre-printed stroke orders. CONCLUSION: WSA material is freely available, and we would encourage the global stroke community to use, and contribute to, its resources.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".