Recasting the role of the surgeon in Uganda: a proposal to maximize the impact of surgery on public health
Bibliographic record
Abstract
A growing body of recent evidence supports the essential role of surgical services in improving population health in low-income countries. Nonetheless, access to surgical services in Uganda, as in many low income countries, is severely limited, largely due to constraints in human resources, infrastructure and supplies. To maximize the impact of surgical services on population health in the context of Uganda's limited surgical workforce, we propose a 'recasting' of the role of the surgeon. Traditionally, the surgeon has played primarily a clinical role in patient care. The demands and isolation of this role have limited the ability of the surgeon to tackle health systems issues related to surgery. Now, the clinical and educational role played by surgeons must be redefined, and the surgeon must also assume a greater role in leadership, management and public health advocacy by documenting the unmet need for surgery and the resources required to improve access to care. Policy and incentives for specialist surgeons to spend amounts of time apportioned to these roles should be developed and supported by health care institutions. Political leadership and commitment will be critical to realizing this ideal. Such a model may be applicable to other countries seeking to maximize the impact of surgical services on population health.
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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 | 0.034 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.026 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".