Global Surgery: The Perspective of Public Health Students
Bibliographic record
Abstract
Current research has emphasized the importance of increased involvement of medical professionals and global health specialists for the success of global surgery efforts. This quantitative descriptive study aimed to examine public health students’ perceptions of global surgery. A 21- question mixed method online survey was distributed over eight weeks via student email to all students enrolled in the Masters of Public Health Program at A.T. Still University (ATSU) College of Graduate Health Studies. Of 212 students, 35 (16.5%) respondents completed the survey with 30 students reporting interest in global health in their future public health careers. Two-thirds of students erroneously identified infectious diseases as the leading cause of death worldwide, not traumatic injury. Participants identified infectious disease and OB/GYN as the two medical fields to contribute significantly to global health. Surgical care was felt to be the least economically cost-effective medical field for low and middle-income countries (LMICs). As the first project to report perspectives of public health students regarding global surgery, this study highlighted several significant misconceptions concerning global surgery. Like the results from similar studies in medical students, it is alarming that there is such a paucity of community health knowledge surrounding surgery and its effects on global surgical needs. Further research should focus on the effect on student perceptions after curriculum modification include education regarding the burden of surgical disease and role of global surgery.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".