Burdens and challenges of hospital-based informal caregiving in Africa: A scoping review
Bibliographic record
Abstract
Introduction: Informal caregivers (ICs) play a crucial role in healthcare, particularly in resource-limited settings where they help alleviate staff shortages. Despite their invaluable contributions, the literature on the challenges of ICs remains sparse. This scoping review addresses this gap by exploring the burdens and challenges faced by those engaged in hospital-based informal caregiving in African contexts. The study focuses on ICs in African hospital environments, acknowledging the unique challenges posed by structured hospital settings. Methods: This scoping review systematically searches relevant literature from 2000 to 2024, concentrating on African hospital settings. Databases including Web of Science, Medline, PsycINFO, SociIndex, CINAHL, Africa-wide, Academic Search Complete, and PubMed were queried. Abstracts were independently assessed for relevance, and potentially eligible studies' full texts were examined by two authors. Papers were selected based on the following inclusion criteria: (1) they reported on the burdens and stress experienced by informal caregivers; (2) they focused on research conducted in Africa; (3) they were published in English; and (4) they were published between January 1, 2000, and August 31, 2024. Results: The review included 26 eligible studies, with a majority conducted in Nigeria (n=6), followed by four each in South Africa and Uganda. Thirteen studies employed qualitative methods, ten utilized quantitative methods, and three adopted mixed methods. Qualitative approaches were primarily interview-based, with limited use of ethnographic methods or group-based techniques like focus group discussions. Family members, including spouses, parents, siblings, and extended relatives, commonly assumed caregiving roles. The themes include the physical health burden of caregiving, socio-economic challenges, emotional distress and social isolation, and family strain. Conclusion: This scoping review provides valuable insights into an overlooked aspect of African healthcare, shedding light on the challenges faced by ICs. By addressing this knowledge gap, it lays the groundwork for future research, policy development, and interventions to support informal caregivers in African hospital settings.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".