Stroke and post-stroke aphasia management in low- and middle-income African countries: a scoping review
Bibliographic record
Abstract
PURPOSE: Stroke is a global health concern, particularly in low- and middle-income countries (LMIC), notably across Africa (LMIAC). Aphasia, a major post-stroke disability emphasizes the importance of effective management services to enhance quality of life of stroke survivors and their families. Concerns exist regarding the adequacy of such services in LMIAC. This scoping review examined stroke and post-stroke aphasia management studies in LMIAC. MATERIALS AND METHODS: Seven electronic databases (PsycINFO (Ovid), MEDLINE (Ovid), PubMed, EMBASE, Cochrane Library, Scopus, and Web of Science) were searched for English peer-reviewed studies (2010- November 2023). Grey literature was sourced from Google and Google Scholar. Search terms included keywords in population, intervention, and geographic area. Titles and abstracts were screened by the lead author and a second reviewer, with conflicts resolved by a third. The lead author conducted full-text screening and grey literature searches, with a second reviewer checking 35% of the articles for eligibility. RESULTS: Sixty-three studies from 19 LMIAC were included; only four studies focused on post-stroke aphasia management. Challenges reported included lack of- knowledge of stroke signs, ambulance services, diagnostic access, and linguistically appropriate post-stroke aphasia resources. CONCLUSION: Effective stroke and post-stroke aphasia management services are needed, along with further LMIAC research.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".