Use of Innovative Technology in Surgical Training in Resource-Limited Settings: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: There has been a rapid growth in interest in global surgery. This increased commitment to improving global surgical care, however, has not translated into an equal exchange of surgical information between high-income countries (HICs) and low-income countries (LMICs). In recent years, a greater emphasis has been placed on training local medical personnel in order to increase surgical capacity while simultaneously decreasing reliance on expatriate visitors. Virtual curricular models, simulators, and immersive technologies have been developed and implemented in order to maximize training opportunities in low-resource settings. This study aims to assess and summarize innovative technologies used for surgical training in low-resource settings. METHODS: We conducted a scoping review of the literature from 2000 to 2021. We included both academic and grey literature on surgical education technologies. Searches were performed on Medline and Embase as well as on Google, iOS, and Android app stores. RESULTS: Four main categories of surgical training platforms were identified: web-based platforms, app-based platforms, virtual and augmented reality, and simulation. The platforms were analyzed based on their content, effectiveness, cost, accessibility, and barriers to use. CONCLUSIONS: Virtual learning platforms show potential in surgical training as they are easily accessible, not limited by geography, continuously updated, and evaluated for effectiveness. In order to provide access to educational resources for surgical trainees all around the world, particularly in low-resource settings, increased effort and resources should be dedicated to developing free, open-access surgical training programs . Doing so will promote sustainable and equitable development in global surgical care.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".