Stroke care in indigenous populations: A World Stroke Organization (WSO) scientific statement
Bibliographic record
Abstract
BACKGROUND: Indigenous Peoples have been reported to experience higher rates of stroke, poorer access to high-quality acute and rehabilitation stroke services, and worse post-stroke outcomes compared to dominant cultures residing in the same countries. The aim of this statement is to summarize available evidence on access barriers contributing to these inequities, effective solutions that have been deployed and tested, and present key recommendations to advance the field. METHODS: We conducted a scoping review searching Medline, Embase, CINHAL, PubMed, Scopus, and Informit Indigenous Collection using the broad search terms "stroke" and "Indigenous" without date restriction until 1 August 2024. We screened 673 unique titles, 96 abstracts, and 80 full-text papers of which we retained 41. We added 10 additional key references known to authors. Articles were analyzed to identify key cross-cutting themes. RESULTS: We identified five key themes: (1) Historical context, colonization and racism; (2) wholistic strength-based approaches to health, well-being, and recovery; (3) communication, health literacy, and cultural safety; (4) Indigenous knowledge systems, research principles, and community-led action; (5) achieving local acceptance versus wide generalizability. RECOMMENDATIONS: Key priority areas, detailed in the form of 11 specific recommendations and based on six core values, include improving stroke service responsiveness, Indigenous Peoples empowerment, and Indigenous research support to better meet the needs of Indigenous Populations globally. The statement has been reviewed and approved by the WSO Executive Committee.
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 | 0.121 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".