The Applicability of eLearning in Community-Based Rehabilitation
Bibliographic record
Abstract
Community-based rehabilitation (CBR) strives to enhance quality of life for individuals with disabilities and their families by increasing social participation and equalizing opportunities in the global south. Aligning with the Sustainable Development Goals, CBR also aims to address the high rates of poverty faced by individuals with disability. Empowerment, a pillar of CBR, involves strengthening the capacity of people with disabilities, their families, and their communities to ensure reduction of disparities. This article outlines a scoping review that guided by the question: “What is known from the existing literature about the applicability of eLearning for capacity building in CBR?” This review did not uncover literature related to eLearning in CBR; however findings suggest that other disciplines, not explicitly tied to CBR, currently use eLearning to educate and empower professionals in the global south. We argue that eLearning technology could be an effective and sustainable solution for CBR programming in the global south for capacity development. Such technology could increase individuals with disabilities’ access to education and could provide opportunities for wider dissemination of knowledge, beyond typical funding cycles. With a goal of informing future CBR practice in eLearning, this article concludes by highlighting key lessons taken from other disciplines that have utilized eLearning in the global south.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".