Human rights-based approach to global surgery: A scoping review
Bibliographic record
Abstract
BACKGROUND: Health is a basic human right, yet surgery remains a neglected stepchild of global health. Worldwide, five billion people lack access to safe, timely, and affordable surgical and anesthesia care when needed. This disparity results in over 18 million preventable deaths each year and is responsible for one-third of the global burden of disease. Here, we evaluate the role of surgical care in protecting human rights and attempt to make a human rights argument for universal access to safe surgical care. MATERIAL AND METHODS: A scoping review was done using the PubMed/MEDLINE, Embase, and Scopus databases to identify articles evaluating human rights and disparities in accessing surgical care globally. A conceptual framework is proposed to implement global surgical interventions with a human rights-based approach. RESULTS: Disparities in accessing surgical care remain prevalent around the world, including but not limited to gender inequality, socioeconomic differentiation, sexual stigmatization, racial and religious disparities, and cultural beliefs. Lack of access to surgery impedes lives in full health and economic prosperity, and thus violates human rights. Our normative framework proposes human rights principles to make surgical policy interventions more inclusive and effective. CONCLUSION: Acknowledging human rights in the provision of surgical care around the world is critical to attain and sustain the Sustainable Development Goals and universal health coverage. National Surgical, Obstetric, and Anesthesia Planning and wider health systems strengthening require the integration of human rights principles in developing and implementing policy interventions to ensure equal and universal access to comprehensive health care services.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".