Cultural Adaptation of an Innovative Reproductive Health Assessment Tool for Dementia Research in Africa: Insights from the Fember‐Africa Study
Bibliographic record
Abstract
BACKGROUND: Dementia research accounts for only 0.1% of all research in Africa, making it the lowest among all low- and middle-income country (LMIC) regions. The development and adaptation of biological and psychosocial measures in ethnically and culturally diverse populations remain limited but are essential for culturally informed research. This is particularly critical for examining sex- and gender-based vulnerabilities to Alzheimer's disease and related dementias (ADRD), including factors such as reproductive health and fertility. METHOD: We conducted a thorough review of our clinical and health questionnaires for cultural relevance and sensitivity through a series (n = 3) of focus groups discussions. These focus groups included a diverse range of participants, such as expert clinical and academic stakeholders, local community members, health promoters, community leaders, and representatives, ensuring a well-rounded and inclusive approach. RESULT: Certain questions about sexual behavior, sexually transmitted diseases, biological and adopted children, and fertility were deemed culturally inappropriate and required rephrasing for sensitivity. To build rapport, these questions were strategically placed after less sensitive topics. Additionally, gaps were identified, including missing questions on traditional fertility practices (e.g., herbal remedies), male puberty characteristics, and partner support during and after childbirth. Addressing these gaps by incorporating local beliefs and traditions will enable a more holistic understanding of reproductive health behaviors. Furthermore, translations overlooked subtle linguistic nuances, highlighting the need for more detailed explanations or alternative concepts in Swahili to ensure clarity and accuracy. CONCLUSION: The Fember-Africa study aims to bridge a critical gap in understanding sex- and gender-specific differences in Africa, shedding light on the disproportionately higher prevalence of dementia among women of African ancestry. Through the culturally sensitive adaptation of reproductive health assessment tool, the study seeks to generate valuable insights that can inform the prevention and management of Alzheimer's disease and related dementias (ADRD) in this underrepresented population.
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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 | 0.047 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".