Unifying a fragmented effort: a qualitative framework for improving international surgical teaching collaborations
Bibliographic record
Abstract
Abstract Background Access to adequate surgical care is limited globally, particularly in low- and middle-income countries (LMICs). To address this issue, surgeons are becoming increasingly involved in international surgical teaching collaborations (ISTCs), which include educational partnerships between surgical teams in high-income countries and those in LMICs. The purpose of this study is to determine a framework for unifying, systematizing, and improving the quality of ISTCs so that they can better address the global surgical need. Methods A convenience sample of 68 surgeons, anesthesiologists, physicians, residents, nurses, academics, and administrators from the U.S., Canada, and Norway was used for the study. Participants all had some involvement in ISTCs and came from multiple specialties and institutions. Qualitative methodology was used, and participants were interviewed using a pre-determined set of open-ended questions. Data was gathered over two months either in-person, over the phone, or on Skype. Data was evaluated using thematic content analysis. Results To organize and systematize ISTCs, participants reported a need for a centralized/systematized process with designated leaders, a universal data bank of current efforts/progress, communication amongst involved parties, full-time administrative staff, dedicated funds, a scholarly approach, increased use of technology, and more research on needs and outcomes. Conclusion By taking steps towards unifying and systematizing ISTCs, the quality of ISTCs can be improved. This could lead to an advancement in efforts to increase access to surgical care worldwide.
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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 | 0.216 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.025 | 0.061 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".