Bridging the know-do gap in low-income surgical environments: Creating contextually appropriate training videos to promote safer surgery in Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although international guidelines exist for the prevention of surgical site infections, their implementation in diverse clinical contexts, especially in low and middle-income countries, is challenging due to the lack of available resources and organizational structure of facilities. The goal of this project was to develop a series of video training aids to highlight best practices in surgical infection prevention in hospitals with limited resources and to provide practical solutions to common challenges faced in these settings. Using the validated Clean Cut education framework for infection prevention developed by Lifebox, a charity devoted to improving surgical and anesthetic safety, we partnered with clinicians in one Ethiopian hospital to create six educational videos giving practical guidelines for infection prevention under resource variable conditions. These include: 1) proper use of the WHO Surgical Safety Checklist, 2) hand and skin antisepsis, 3) confirming instrument sterility, 4) maintaining the sterile field, 5) antibiotic prophylaxis, and 6) gauze counting. Gaps in available online educational materials were identified in each of the six areas. Videos were created providing setting-specific education and addressing gaps in existing materials for each of the infection prevention topics. These videos are now integrated into infection prevention curricula through Lifebox in Ethiopia and ongoing data collection to evaluate acceptability and efficacy is ongoing. Surgical education videos on infection prevention topics addressing location-specific resources and workarounds can be useful to hospitals operating in resource-limited settings for training staff and supporting quality and safety efforts in surgery.
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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 | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it