Barriers to participation in global surgery academic collaborations, and possible solutions: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: There is a global lack of access to surgical care, and this issue disproportionately affects those in low- and middle-income countries. Global surgery academic collaborations (GSACs) between surgeons in high-income countries and those in low- and middle-income countries are one possible sustainable way to address the global surgical need. The objective of this study was to examine the barriers to participation in GSACs and to suggest ways to increase involvement. METHODS: A convenience sample of 86 surgeons, anesthesiologists, other physicians, residents, fellows, and nurses from the US, Canada, and Norway was used. Participants were all health care providers from multiple specialties and multiple academic centers with varied involvement in GSACs. More than half of the participants were neurosurgeons. Participants were interviewed in person or over Skype in Toronto over the course of 2 months by using a predetermined set of open-ended questions. Thematic content analysis was used to evaluate the participants' responses. RESULTS: Based on the data, 3 main themes arose that pointed to individual, community, and system barriers for involvement in GSACs. Individual barriers included loss of income, family commitments, young career, responsibility to local patients, skepticism of global surgery efforts, ethical concerns, and safety concerns. Community barriers included insufficient mentorship and lack of support from colleagues. System barriers included lack of time, minimal academic recognition, insufficient awareness, insufficient administrative support and organization, and low political and funding support. CONCLUSIONS: Steps can be taken to address some of these barriers and to increase the involvement of surgeons from high-income countries in GSACs. This could lead to a necessary scale-up of global surgery efforts that may help increase worldwide access to surgical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".