Role of Collaborative Academic Partnerships in Surgical Training, Education, and Provision
Bibliographic record
Abstract
The global disparities in both surgical disease burden and access to delivery of surgical care are gaining prominence in the medical literature and media. Concurrently, there is an unprecedented groundswell in idealism and interest in global health among North American medical students and trainees in anesthesia and surgical disciplines. Many academic medical centers (AMCs) are seeking to respond by creating partnerships with teaching hospitals overseas. In this article we describe six such partnerships, as follows: (1) University of California San Francisco (UCSF) with the Bellagio Essential Surgery Group; (2) USCF with Makerere University, Uganda; (3) Vanderbilt with Baptist Medical Center, Ogbomoso, Nigeria; (4) Vanderbilt with Kijabe Hospital, Kenya; (5) University of Toronto, Hospital for Sick Children with the Ministry of Health in Botswana; and (6) Harvard (Brigham and Women's Hospital and Children's Hospital Boston) with Partners in Health in Haiti and Rwanda. Reflection on these experiences offers valuable lessons, and we make recommendations of critical components leading to success. These include the importance of relationships, emphasis on mutual learning, the need for "champions," affirming that local training needs to supersede expatriate training needs, the value of collaboration in research, adapting the mission to locally expressed needs, the need for a multidisciplinary approach, and the need to measure outcomes. We conclude that this is an era of cautious optimism and that AMCs have a critical opportunity to both shape future leaders in global surgery and address the current global disparities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.034 | 0.022 |
| Open science | 0.004 | 0.053 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".