Strategies and Recommendations to Improve Accessibility of Essential Surgery in Rural Settings in OECD Countries: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The provision of essential and emergency surgical services presents complex challenges in remote areas. Equitable access has gained attention thanks to the significant work done by the Lancet Commission on Global Surgery (LCoGS). Although the focus was on low- and middle-income countries, developed countries also face challenges in providing equitable surgical care and, in fact, do not always meet the benchmarks set by the LCoGS yet still have acceptable outcomes. We sought to explore the current strategies aimed at improving and maintaining access to essential surgical care in rural and remote areas of OECD countries (Organization for Economic Co-operation and Development). METHODS: We conducted a scoping review using MeSH terms. The search was performed on MEDLINE and EMBASE databases and was limited to English sources published between 1946 and January 10, 2025. ELIGIBILITY CRITERIA: Any strategy or intervention aimed at improving and maintaining timely access to essential surgeries in rural and remote areas of OECD countries. RESULTS: Six main categories of strategies were found: (1) resource distribution; (2) task sharing; (3) telemedicine; (4) surgical workforce; (5) training and education; and (6) prehospital system. CONCLUSION: Recognizing that developed countries, in fact, do not always meet the benchmarks set by the LCoGS yet still have acceptable outcomes highlights that specific strategies are important contributors to the reduction of disparities between rural and urban outcomes. These strategies may be used in the study of surgical services in low- and middle-income countries.
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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 | 0.020 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".