Ethical considerations in global surgery: a scoping review
Bibliographic record
Abstract
Introduction: An unmet burden of surgical disease exists worldwide and is disproportionately shouldered by low-income and middle-income countries (LMICs). As the field of global surgery grows to meet this need, ethical considerations need to be addressed. Currently, there are no formal guidelines to help inform relevant stakeholders of the ethical challenges and considerations facing global surgical collaborations. The aim of this scoping review is to synthesise the existing literature on ethics in global surgery and identify gaps in the current knowledge. Methods: A scoping review of relevant databases to identify the literature pertaining to ethics in global surgery was performed. Eligible articles addressed at least one ethical consideration in global surgery. A grounded theory approach to content analysis was used to identify themes in the included literature and guide the identification of gaps in existing literature. Results: Four major ethical domains were identified in the literature: clinical care and delivery; education and exchange of trainees; research, monitoring and evaluation; and engagement in collaborations and partnerships. The majority of published literature related to issues of clinical care and delivery of the individual patient. Most of the published literature was published exclusively by authors in high-income countries (HICs) (80%), and the majority of articles were in the form of editorials or commentaries (69.1%). Only 12.7% of articles published were original research studies. Conclusion: The literature on ethics in global surgery remains sparse, with most publications coming from HICs, and focusing on clinical care and short-term surgical missions. Given that LMICs are frequently the recipients of global surgical initiatives, the relative absence of literature from their perspective needs to be addressed. Furthermore, there is a need for more literature focusing on the ethics surrounding sustainable collaborations and partnerships.
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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 | 0.050 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".