Global Surgery 2030: a roadmap for high income country actors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Millennium Development Goals have ended and the Sustainable Development Goals have begun, marking a shift in the global health landscape. The frame of reference has changed from a focus on 8 development priorities to an expansive set of 17 interrelated goals intended to improve the well-being of all people. In this time of change, several groups, including the Lancet Commission on Global Surgery, have brought a critical problem to the fore: 5 billion people lack access to safe, affordable surgical and anaesthesia care when needed. The magnitude of this problem and the world's new focus on strengthening health systems mandate reimagined roles for and renewed commitments from high income country actors in global surgery. To discuss the way forward, on 6 May 2015, the Commission held its North American launch event in Boston, Massachusetts. Panels of experts outlined the current state of knowledge and agreed on the roles of surgical colleges and academic medical centres; trainees and training programmes; academia; global health funders; the biomedical devices industry, and news media and advocacy organisations in building sustainable, resilient surgical systems. This paper summarises these discussions and serves as a consensus statement providing practical advice to these groups. It traces a common policy agenda between major actors and provides a roadmap for maximising benefit to surgical patients worldwide. To close the access gap by 2030, individuals and organisations must work collectively, interprofessionally and globally. High income country actors must abandon colonial narratives and work alongside low and middle income country partners to build the surgical systems of the future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it