Addressing the challenges of dementia care in Nigeria: A call for a comprehensive national strategy
Bibliographic record
Abstract
Despite the growing prevalence of dementia, driven by an ageing population and compounded by cultural misunderstandings and stigma, Nigeria lacks a coherent national plan to address this issue. The article points out that although Nigeria has enacted policies such as the National Aging Policy, which do not sufficiently address the specific needs of people living with dementia. It underscores the necessity of integrating a dementia strategy within the broader health and social care systems of Nigeria. The article draws on the World Health Organization's Global Dementia Action Plan to elaborates on several critical areas for action, including public health prioritization of dementia, awareness and stigma reduction, improved diagnosis, treatment, care, and support, alongside bolstering support for caregivers. It stresses the importance of a dementia-friendly infrastructure, research and innovation, and leveraging community support to foster a more inclusive society. Furthermore, the article outlines current state of healthcare and social support systems in Nigeria, pointing to significant gaps in infrastructure, healthcare workforce, and financial mechanisms that hinder effective dementia care. Hence, elevating dementia care as a national health priority, and increasing access to quality care and support, Nigeria is well positioned to mitigate the impact of dementia on families and the person with dementia. The call to action is clear: a national dementia strategy, informed by global best practices and tailored to Nigeria's unique cultural and societal context, is essential for addressing the challenges of dementia care and improving the well-being of affected individuals and their families in Nigeria.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".