A case study of global health at the university: implications for research and action
Bibliographic record
Abstract
BACKGROUND: Global health is increasingly a major focus of institutions in high-income countries. However, little work has been done to date to study the inner workings of global health at the university level. Academics may have competing objectives, with few mechanisms to coordinate efforts and pool resources. OBJECTIVE: To conduct a case study of global health at Canada's largest health sciences university and to examine how its internal organization influences research and action. DESIGN: We drew on existing inventories, annual reports, and websites to create an institutional map, identifying centers and departments using the terms 'global health' or 'international health' to describe their activities. We compiled a list of academics who self-identified as working in global or international health. We purposively sampled persons in leadership positions as key informants. One investigator carried out confidential, semi-structured interviews with 20 key informants. Interview notes were returned to participants for verification and then analyzed thematically by pairs of coders. Synthesis was conducted jointly. RESULTS: More than 100 academics were identified as working in global health, situated in numerous institutions, centers, and departments. Global health academics interviewed shared a common sense of what global health means and the values that underpin such work. Most academics interviewed expressed frustration at the existing fragmentation and the lack of strategic direction, financial support, and recognition from the university. This hampered collaborative work and projects to tackle global health problems. CONCLUSIONS: The University of Toronto is not exceptional in facing such challenges, and our findings align with existing literature that describes factors that inhibit collaboration in global health work at universities. Global health academics based at universities may work in institutional siloes and this limits both internal and external collaboration. A number of solutions to address these challenges are proposed.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.044 | 0.022 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| 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".