Towards defining the surgical workforce for children: a geospatial analysis in Brazil
Bibliographic record
Abstract
OBJECTIVES: The optimal size of the health workforce for children's surgical care around the world remains poorly defined. The goal of this study was to characterise the surgical workforce for children across Brazil, and to identify associations between the surgical workforce and measures of childhood health. DESIGN: ). SETTINGS AND PARTICIPANTS: We collected data on the surgical workforce (paediatric surgeons, general surgeons, anaesthesiologists and nursing staff), perioperative mortality rate (POMR) and under-5 mortality rate (U5MR) across Brazil for 2015. PRIMARY AND SECONDARY OUTCOME MEASURES: We performed descriptive analyses, and identified associations between the workforce and U5MR using geospatial analysis (Getis-Ord-Gi analysis, spatial cluster analysis and linear regression models). FINDINGS: There were 39 926 general surgeons, 856 paediatric surgeons, 13 243 anaesthesiologists and 103 793 nurses across Brazil in 2015. The U5MR ranged from 11 to 26 deaths/1000 live births and the POMR ranged from 0.11-0.17 deaths/100 000 children across the country. The surgical workforce is inequitably distributed across the country, with the wealthier South and Southeast regions having a higher workforce density as well as lower U5MR than the poorer North and Northeast regions. Using linear regression, we found an inverse relationship between the surgical workforce density and U5MR. An U5MR of 15 deaths/1000 births across Brazil is associated with a workforce level of 5 paediatric surgeons, 200 surgeons, 100 anaesthesiologists or 700 nurses/100 000 children. CONCLUSIONS: We found wide disparities in the surgical workforce and childhood mortality across Brazil, with both directly related to socioeconomic status. Areas of increased surgical workforce are associated with lower U5MR. Strategic investment in the surgical workforce may be required to attain optimal health outcomes for children in Brazil, particularly in rural regions.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".