Bridging the know-do gap in low-income surgical environments: Creating contextually appropriate training videos to promote safer surgery in Ethiopia
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".