Digital Rehabilitation Interventions in Sub-Saharan Africa: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Estimations show that at least one in every 3 people in the world needs rehabilitation at some point in the course of their illness or injury. Access to rehabilitation services is an essential part of the continuum of care and is integral to achieving universal health coverage. However, most of the world's population living in low- and middle-income countries, especially in the sub-Saharan African region, does not have access to adequate rehabilitation services. Wider adoption of digital solutions offers opportunities to support and enhance access to rehabilitation services in sub-Saharan Africa. A region where there is a greater burden and need for these services. There is also little published research about digital rehabilitation in sub-Saharan Africa, as it is an underexamined topic in the region. OBJECTIVE: This scoping review aims to provide a comprehensive picture of the current evidence of digital interventions in rehabilitation implemented in any health, social, educational, or community setting in the sub-Saharan Africa region. METHODS: We will conduct a scoping review using Arksey and O'Malley's methodological framework and follow the Joanna Briggs Institute methodology for scoping reviews. We will develop search strategies for a selected number of web-based databases, search for peer-reviewed scientific publications until September 2023, and screen the reference lists of relevant articles. We will include research articles if they describe or report the use of digital interventions in the rehabilitation of patients with any health problem or disability in sub-Saharan Africa. For selected articles, we will extract data using a customized data extraction form and use thematic analysis to compare the findings across studies. RESULTS: The preliminary database search in MEDLINE (EBSCO) was completed in May 2023. The research team will conduct a search of relevant articles in the autumn. The results will be synthesized and reported under the key conceptual categories of this review, and we expect the final scoping review to be ready for submission in early 2024. CONCLUSIONS: We expect to find gaps in the research and a lack of detailed information about digital rehabilitation interventions in sub-Saharan Africa, as well as potential areas for further study. We will identify opportunities to inform the development of digital rehabilitation interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48952.
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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 | 0.081 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.091 | 0.019 |
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".