Defining a Framework and Evaluation Metrics for Sustainable Global Surgical Partnerships
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to use expert consensus to build a concrete and realistic framework and checklist to evaluate sustainability in global surgery partnerships (GSPs). BACKGROUND: Partnerships between high-resourced and low-resourced settings are often created to address the burden of unmet surgical need. Reflecting on the negative, unintended consequences of asymmetrical partnerships, global surgery community members have proposed frameworks and best practices to promote sustainable engagement between partners, though these frameworks lack consensus. This project proposes a cohesive, consensus-driven framework with accompanying evaluation metrics to guide sustainability in GSPs. METHODS: A modified Delphi technique with purposive sampling was used to build consensus on the definitions and associated evaluation metrics of previously proposed pillars (Stakeholder Engagement, Multidisciplinary Collaboration, Context-Relevant Education and Training, Bilateral Authorship, Multisource Funding, Outcome Measurement) of sustainable GSPs. RESULTS: Fifty global surgery experts from 34 countries with a median of 9.5 years of experience in the field of global surgery participated in 3 Delphi rounds. Consensus was achieved on the identity, definitions, and a 47-item checklist for the evaluation of the 6 pillars of sustainability in GSPs. In all, 29% of items achieved consensus in the first round, whereas 100% achieved consensus in the second and third rounds. CONCLUSIONS: We present the first framework for building sustainable GSPs using the input of experts from all World Health Organization regions. We hope this tool will help the global surgery community to find noncolonial solutions to addressing the gap in access to quality surgical care in low-resource settings.
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.
How this classification was reachedexpand
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".