Making meaning through cultural embeddedness: Advancing culture‐sensitive training for Alzheimer’s Disease and Alzheimer’s Disease Related Dementia Caregivers in Ibadan, Nigeria
Bibliographic record
Abstract
Abstract Background Limited knowledge exists about the cultural approaches to managing the psychological and behavioral outcomes (PBO) of Alzheimer’s Disease and Alzheimer’s Disease‐Related Dementia (AD/ADRD) in Africa. Specifically, to develop a culture‐sensitive training framework for AD/ADRD caregivers, we explored AD/ADRD caregivers’ cultural embeddedness in managing people living with AD/ADRD (PLWAD/ADRD) in Ibadan, Nigeria. Method Using hermeneutic phenomenological qualitative research design, we interviewed 23 caregivers providing care to PLWAD/ADRD in Ibadan, a Yoruba‐speaking ethnic group of Nigeria. Caregivers participated in in‐depth interviews (IDI) to share their experiences in managing AD/ADRD PBO. To capture the interpretive value of the caregivers’ experiences, we developed inductive and deductive codes and applied them to caregivers’ narratives using Nvivo 14. Based on the reflexivity of the multidisciplinary research team, themes, and sub‐themes about the cultural elements of caregivers' response to the PBO of PLWAD/ADRD were developed. Result Among the 23 caregivers, 16 were female; five received a monthly salary; two were non‐members of the Yoruba ethnic group. We found five culturally sensitive themes, broadly categorized into cultural universals and specifics. The Yoruba‐specific aspects included (1) respect and filial piety, (2) non‐verbal cues, and (3) communal living arrangements and rotational care. Meanwhile, the cultural universals centered on (4) reciprocity/fear of retribution and (5) patience. Caregivers showed respect and filial piety by staying quiet, speaking in a low tone, minimizing correction, and refraining from commenting on PLWAD/ADRDs' self‐efficacy. Non‐verbal cues, like facial expressions, played a crucial role. The communal living arrangement proved valuable in managing negative behaviors like wandering. Reciprocity and retribution were embedded beliefs, and patience was a culturally universal approach requisite in the care of PLWAD/ADRD. Conclusion The five cultural themes are important training areas for managing the behavioral and psychological outcomes of AD/ADRD. Mixed‐methods studies are required to explore the dimensions and generalizability of the five cultural constructs to strengthen Africa’s AD/ADRD caregiving workforce.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".