Abstract DP6: Multicultural Recommendations to Guide Stroke Care: A Document Review of International Stroke Guidelines
Bibliographic record
Abstract
Introduction: Stroke represents a major global public health challenge, with 12.2 million new cases, 6.5 million deaths, and over 143 million disabilities occurring annually, leading to significant economic and social repercussions. Structural racism deleteriously influences stroke care and outcomes, making it essential to integrate multicultural considerations throughout the stroke care continuum to enhance outcomes and reduce disparities. A document analysis was conducted to assess the extent to which stroke guidelines address cultural diversity in stroke care. Method: A document review of international stroke guidelines was employed based on the framework established by Steinberg et al. A Google search was conducted to identify international stroke care guidelines published in English within the last five years (2019 – 2024). The quality of these guidelines was assessed using the Appraisal of Guidelines for Research&Evaluation (AGREE-II) tool. Paired reviewers independently screened the guidelines and identified recommendations related to multicultural stroke care, defined as ‘ practices aimed at ensuring care delivery is culturally competent, sensitive, safe, equitable, and adaptable’ . Results: A total of twenty-five guidelines were included, with the majority originating from Western countries such as Australia, the United States, the United Kingdom, Canada, and various European societies. Only four of the twenty-five guidelines explicitly addressed multicultural considerations. Most of the recommendations were based on low levels of evidence or consensus and related to dietary and cultural considerations, and the management of diverse patient needs in stroke care. Conclusion: A paucity of recommendations for multicultural considerations in stroke care were identified. Current stroke care guidelines fail to address multiculturalism adequately, which may reflect the maturity of the available evidence to inform guideline recommendations. Further research is needed to identify enablers and barriers to incorporating multicultural considerations in stroke care and the development of future guidelines. New evidence is needed in this regard to improve clinical outcomes of diverse populations.
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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 | 0.085 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".