The international partner universities of East African health professional programmes: why do they do it and what do they value?
Bibliographic record
Abstract
BACKGROUND: Globalization and funding imperatives drive many universities to internationalize through global health programmes. University-based global health researchers, advocates and programmes often stress the importance of addressing health inequity through partnerships. However, empirical exploration of perspectives on why universities engage in these partnerships and the benefits of them is limited. OBJECTIVE: To analyse who in international partner universities initiated the partnerships with four East African universities, why the partnerships were initiated, and what the international partners value about the partnerships. METHODS: Fifty-nine key informants from 26 international universities partnering with four East African universities in medicine, nursing and/or public health participated in individual in-depth interviews. Transcripts were analysed thematically. We then applied Burton Clark's framework of "entrepreneurial" universities characterized by an "academic heartland", "expanded development periphery", "managerial core" and "expanded funding base", developed to examine how European universities respond to the forces of globalization, to interpret the data through a global health lens. RESULTS: Partnerships that were of interest to universities' "academic heartland" - research and education - were of greatest interest to many international partners, especially research intensive universities. Some universities established and placed coordination of their global health activities within units consistent with an expanded development periphery. These units were sometimes useful for helping to establish and support global health partnerships. Success in developing and sustaining the global health partnerships required some degree of support from a strengthened steering or managerial core. Diversified funding in the form of third-stream funding, was found to be essential to sustain partnerships. Social responsibility was also identified as a key ethos required to unite the multiple elements in some universities and sustain global health partnerships. CONCLUSION: Universities are complex entities. Various elements determine why a specific university entered a specific international partnership and what benefits it accrues. Ultimately, integration of the various elements is required to grow and sustain partnerships potentially through embracing social responsibility as a common value.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".