Mapping International University Partnerships Identified by East African Universities as Strengthening Their Medicine, Nursing, and Public Health Programs
Bibliographic record
Abstract
BACKGROUND: International university partnerships are recommended for increasing the capacity of sub-Saharan African universities. Many publications describe individual partnerships and projects, and tools are available for guiding collaborations, but systematic mappings of the basic, common characteristics of partnerships are scarce. OBJECTIVE: To document and categorize the international interuniversity partnerships deemed significant to building the capacity of medicine, nursing, and public health programs of 4 East African universities. METHODS: Two universities in Kenya and 2 in Tanzania were purposefully selected. Key informant interviews, conducted with 42 senior representatives of the 4 universities, identified partnerships they considered significant for increasing the capacity of their institutions' medicine, nursing, and public health programs in education, research, or service. Interviews were transcribed and analyzed. Partners were classified by country of origin and corresponding international groupings, duration, programs, and academic health science components. FINDINGS: One hundred twenty-nine university-to-university partnerships from 23 countries were identified. Each university reported between 25 and 36 international university partners. Seventy-four percent of partnerships were with universities in high-income countries, 15% in low- and middle-income countries, and 11% with consortia. Seventy percent included medicine, 37% nursing, and 45% public health; 15% included all 3 programs. Ninety-two percent included an education component, 47% research, and 24% service; 12% included all 3 components. CONCLUSIONS: This study confirms the rapid growth of interuniversity cross-border health partnerships this century. It also finds, however, that there is a pool of established international partnerships from numerous countries at each university. Most partnerships that seek to strengthen universities in East Africa should likely ensure they have a significant education component. Universities should make more systematic information about past and existing partnerships available publicly.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".