Use of surgical task shifting to scale up essential surgical services: a feasibility analysis at facility level in Uganda
Bibliographic record
Abstract
BACKGROUND: The shortage and mal-distribution of surgical specialists in sub-Saharan African countries is born out of shortage of individuals choosing a surgical career, limited training capacity, inadequate remuneration, and reluctance on the part of professionals to work in rural and remote areas, among other reasons. This study set out to assess the views of clinicians and managers on the use of task shifting as an effective way of alleviating shortages of skilled personnel at a facility level. METHODS: 37 in-depth interviews with key informants and 24 focus group discussions were held to collect qualitative data, with a total of 80 healthcare managers and frontline health workers at 24 sites in 15 districts. Quantitative and descriptive facility data were also collected, including operating room log sheets to identify the most commonly conducted operations. RESULTS: Most health facility managers and health workers supported surgical task shifting and some health workers practiced it. The practice is primarily driven by a shortage of human resources for health. Personnel expressed reluctance to engage in surgical task shifting in the absence of a regulatory mechanism or guiding policy. Those in favor of surgical task shifting regarded it as a potential solution to the lack of skilled personnel. Those who opposed it saw it as an approach that could reduce the quality of care and weaken the health system in the long term by opening it to unregulated practice and abuse of privilege. There were enough patient numbers and basic infrastructure to support training across all facilities for surgical task shifting. CONCLUSION: Whereas surgical task shifting was viewed as a short-term measure alongside efforts to train and retain adequate numbers of surgical specialists, efforts to upscale its use were widely encouraged.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".