The University of Gondar, Queen’s University and Mastercard Foundation Scholars Program: A partnership for disability-inclusive higher education in Ethiopia
Bibliographic record
Abstract
This article describes the development and implementation process of an innovative 10-year partnership that draws on the strengths of existing community-based rehabilitation programs to support new education and leadership development activities in Ethiopia. Current global estimates indicate that over 17 million people may be affected by disability in Ethiopia. The national population projection for 2017 indicates that approximately 80 per cent of the population resides in underserved rural areas, with limited to no access to necessary health, rehabilitation, or social services. The University of Gondar (UoG) in Ethiopia has been serving people with disabilities in and around the North Gondar Zone since its inception in the mid-1950s. Over the years, its various units have designed and implemented numerous projects, employing alternative institutional and community-based models to promote the wellbeing of people with disabilities. Lessons drawn from these initiatives and shifts in health and social work practice informed UoG’s decision to establish its Community-Based Rehabilitation (CBR) program in 2005. Given a shared commitment to the principles and practice of CBR, the UoG is presently collaborating with the International Centre for the Advancement of Community Based Rehabilitation (ICACBR) at Queen’s University in Canada to create new disability-related education and mentorship opportunities. These include community-based research and internship opportunities for undergraduate and graduate scholars through a shared Mastercard Foundation Scholars Program. The two institutions, in collaboration with the Mastercard Foundation, have an overall goal of creating a disability-inclusive campus and regional rehabilitation hub at UoG. In this article, the authors discuss the unique collaborative structure of project management and implementation, and the embeddedness of university-community engagement to meet project objectives informed by the North–South/South–North partnership models. They also provide critical insights to, and reflections on, the challenges inherent in international, interdisciplinary university-community collaboration and the benefits from enhancing higher education in both Ethiopia and Canada. In contrast to shorter term or smaller projects that rely heavily on individual champions, this article focuses on larger scale, process-oriented institutional learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".