Adoption of Telemedicine for Dementia Care in Nigeria: Scoping Review
Bibliographic record
Abstract
Background: Dementia is a global health challenge, particularly in Nigeria, where limited health care infrastructure, cultural stigmas, and poor awareness hinder its care. Telemedicine can improve patient outcomes, increase health care access, and support caregivers. However, challenges such as poor internet connectivity, digital literacy, and a lack of integrated strategies hinder its adoption, particularly in rural areas. Objective: This scoping review aims to evaluate the adoption of telemedicine for dementia care in Nigeria by highlighting existing interventions, their effectiveness, implementation challenges, and contextual barriers. It also draws on global evidence to propose culturally relevant, sustainable strategies. Methods: A scoping review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) framework. Peer-reviewed articles were included if they focused on telemedicine or digital health interventions for dementia care in Nigeria or sub-Saharan Africa and published between January 2010 and February 2024. Databases searched included PubMed, Scopus, CINAHL, PsycINFO, Cochrane Library, and Google Scholar. A total of 23 articles met the inclusion criteria. Results: Among the 23 studies, 10 (43.5%) focused on mobile health apps, 8 (34.8%) on video consultations, and 5 (21.7%) on remote monitoring tools. These interventions improved caregiver support, medication adherence, and access to specialist care. Key barriers included limited digital literacy, poor internet access, and a lack of cohesive national telemedicine policy. Conclusions: There is an urgent need for an inclusive national telemedicine policy in Nigeria. Interventions such as mobile health, video consultations, and remote monitoring tools show potential to enhance dementia care, reduce caregiver burden, and improve health outcomes.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".