Building global surgical workforce capacity through academic partnerships
Bibliographic record
Abstract
Abstract: Nearly 5 billion of the world’s growing population lacks access to safe, accessible and equitable surgical care. It results in millions of disabilities and death due to common diseases treated surgically. The severe shortage of the surgical workforce, as well as the unequal distribution of providers in urban, compared with rural areas, is a challenge faced by many communities. Global surgery academic partnerships between institutions in high-income countries (HICs) and low-middle income countries have played an essential role in developing surgical workforce capacity. There is also an increased interest from students and trainees in HICs to partake in international training opportunities. However, not all partnerships are equal and sometimes raise critical ethical concerns. Various recommendations have been made to define and create equitable, sustainable and ethical collaborations that focus on the priorities of the low-middle-income country (LMIC) institutions and trainees. In this article, we review some of the academic partnerships that exist and other training models that provide sustainable and accessible education and resources for mutual learning between surgical trainees from both high-income and low-middle income countries. There is an overwhelming need for high-income and low-income institutions to work together to create equitable and ethical partnerships and build a workforce to provide safe and accessible surgery for all.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".