Challenges to Rehabilitation Services in Sub-Saharan Africa From a User, Health System, and Service Provider Perspective: Scoping Review
Bibliographic record
Abstract
Background: Rehabilitation aims to restore and optimize the functioning of impaired systems for people with disabilities. It is an integral part of universal health coverage, and access to it is a human right. Objective: We aimed to identify the key challenges to rehabilitation services in Sub-Saharan Africa from a user, health system, and service provider perspective. Methods: This scoping review was conducted in accordance with the 5-stage framework proposed by Arksey and O'Malley. A comprehensive electronic search was run to identify published articles on rehabilitation services in Sub-Saharan Africa. Of the 131 articles retrieved, 83 articles were assessed for eligibility and 15 papers that met the inclusion criteria were considered. Results: The results show that people with disabilities in Sub-Saharan Africa face multifactorial challenges to access rehabilitation services. Poor access to rehabilitation services is associated with less attention given to rehabilitation by governments, which leads to less funding, negative cultural and social beliefs, fewer rehabilitation centers, poorly equipped rehabilitation units, failure of health systems, lack of training to rehabilitation practitioners, and logistical and financial constraints. This review also reveals that digital rehabilitation reduces costs and improves access to services in hard-to-reach geographical areas. However, digital rehabilitation faces challenges as well, including connectivity issues, inaccessibility to technology, a lack of technical knowledge, a lack of privacy, and ethical concerns. Conclusions: People with disabilities face multifactorial challenges to access rehabilitation services in Sub-Saharan Africa. It is therefore critical to address these challenges to optimize patients' health outcomes and offer better rehabilitation services.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".