Whispers of Memory: How families navigate Dementia support beyond the Biomedical Lens in Nigeria
Bibliographic record
Abstract
BACKGROUND: Research on the experiences of caring for persons with dementia in African regions has tended to focus on caregivers' perceptions and experiences of using biomedical services and supports such as hospital centres. While valuable, this focus provides an incomplete picture, neglecting the broader network of support that caregivers rely upon. This study aims to investigate how all systems of support accessed by Yoruba caregivers in Nigeria work together to shape understanding of dementia and caregiving experiences. METHOD: This study used the interpretive phenomenology approach to understand the lived experiences of Yoruba families supporting persons with dementia (n = 15). Semi-structured interviews (family dialogue) were conducted in the houses of these families in the presence of persons with dementia. All interviews were audio-recorded, transcribed, translated, and reflexively analyzed using emergent themes. RESULT: While urban families (n = 11) benefited from structured hospital care (n = 10), rural families (4) relied heavily on traditional/faith-based healers, neighbours, landlord associations and community elders. Families tended to seek help only in the later stages of dementia when behaviours had become more challenging. The types of services accessed appeared to be influenced by perceptions of dementia, geographical location (rural versus urban) and the advice given by trusted others. No matter where families sought assistance, they tended to feel supported when they received practical tips and guidance on how to best support their relatives with dementia. Those accessing traditional or faith-based healing reported the additional benefits of spiritual guidance, hope, and a sense of community belonging. CONCLUSION: Our findings suggest the importance of improving dementia awareness amongst Yoruba caregivers in Nigeria so that help can be accessed sooner. It further suggests that improving caregivers' access to a variety of faith-based traditional and medical supports may go a long way in improving caregivers' experiences in supporting persons with dementia.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".