Patterns of Elder Caregiving Among Nigerians: An Integrative Review
Bibliographic record
Abstract
This integrative review on patterns of elder caregiving in Nigeria synthesizes evolving dynamics and determinants of caregiving practices amid demographic and household change. The objective of this review was to identify prevalent patterns of elder caregiving, explore the roles and responsibilities of caregivers, and examine the challenges and support needs within the Nigerian context. Academic Search Complete, CINAHL, PubMed, PsycINFO, and Medline were searched in November 2024. Inclusion criteria were peer-reviewed journal articles published in English focusing on elder caregiving among Nigerians; non-peer-reviewed sources (e.g., dissertations, conference papers, and books) were excluded. Data extraction was performed using a structured matrix, and findings were synthesized thematically. Risk of bias was appraised using SANRA (for narrative reviews) and MMAT (for empirical studies). Twenty studies published between 1991 and December 2022 were included. Analyses were guided by an intersectional conceptual framework spanning five domains: cultural, familial, economic, psychosocial, and policy. The interconnected dimensions illustrate how cultural expectations shape family caregiving roles, which in turn influence economic strain, emotional well-being, and access to institutional support. By emphasizing the interaction among gender, class, and social location within these domains, the framework demonstrates how caregiving operates as a multidimensional and relational process. Thematic synthesis identified six overarching themes: cultural influences, gender differences, family dynamics, economic factors, challenges faced by Nigerian caregivers, and government policies and support. Limitations include reliance on single-reviewer screening and extraction, exclusion of unpublished and non-peer-reviewed sources, restriction to English-language studies, and a focus on the Nigerian context, which may limit generalizability. Findings underscore that elder caregiving in Nigeria is multifaceted and shaped by intersecting gendered, cultural, and economic forces. Policy and practice should prioritize caregiver supports, accessible geriatric services, and gender-sensitive interventions, while future research applies the framework to address gaps in transnational and multilingual evidence.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".