Supporting informal older adult caregivers in Nigeria: Recommendations for policy
Bibliographic record
Abstract
The aim of this study was to understand the roles of informal caregivers in Nigeria and how to support them in providing quality care for older adults. Despite their indispensable contributions, informal caregivers encounter numerous challenges, including physical and emotional strain, financial constraints, and a lack of recognition and support. Currently, Nigeria lacks specific policies supporting informal caregivers, making it imperative to establish comprehensive measures addressing their needs. This study adopted a systematic review approach using secondary data resources from reputable data bases such as Google scholar, PubMed and African Journals Online. The Ujama African theory was used as a theoretical framework. The cultural, economic, and systemic factors influencing informal caregiving in Nigeria impacts the quality of care provided to older adults and the wellbeing of caregivers. The findings showed the need for families, social workers and government to provide financial support, respite, training, education, and access to healthcare services for caregivers. It was recommended that policy should not only acknowledge the significance of informal caregivers but also offer the necessary support to ensure the well-being of both caregivers and the older population they serve. Older adults and their caregivers should be considered while implementing social support and care systems in Nigeria. Current and previous volumes are available at: https://ajsw.africasocialwork.net HOW TO REFERENCE USING ASWDNET STYLEIkeorji, C. R. & Ubani, T. C. (2024). Supporting informal older adult caregivers in Nigeria: recommendations for policy. African Journal of Social Work, 14(6), 311-320. https://dx.doi.org/10.4314/ajsw.v14i6.2
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".