A Framework for the Monitoring and Evaluation of International Surgical Initiatives in Low- and Middle-Income Countries
Bibliographic record
Abstract
BACKGROUND: An estimated two billion people worldwide lack adequate access to surgical care. To address this humanitarian emergency, an increasing number of international surgical partnerships are emerging between developed and low- and middle-income countries (LMICs). At present, there are no clear indicators that may be used to assess the effectiveness of such initiatives. STUDY DESIGN: We conducted an international qualitative study of 31 surgeons from developed and LMICs involved in international partnerships across a variety of subspecialties. Thematic analysis and grounded theory were applied in order to develop a practical framework that may be applied to monitor and evaluate global surgical initiatives. RESULTS: Several themes emerged from the study: (i) there is a large unmet need to establish and maintain prospective databases in LMICs to inform the monitoring and evaluation of international surgical partnerships; (ii) assessment of initiatives must occur longitudinally over the span of several years; (ii) the domains of assessment are contextual and encompass cultural, institutional and regional factors; and (iv) evaluation strategies should explore broader impact within the community and country. Based on thematic analysis within the domains of inputs, outputs and outcomes, a framework for the monitoring and evaluation of international surgical initiatives, the Framework for the Assessment of InteRNational Surgical Success (FAIRNeSS) is proposed. CONCLUSIONS: In response to the increasing number of surgical partnerships between developed and LMICs, we propose a framework to monitor and evaluate international surgical initiatives.
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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 | 0.374 | 0.230 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.019 | 0.065 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.010 | 0.029 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".