Global Surgery - a Review of the Paediatric Surgical Workforce in South Africa
Bibliographic record
Abstract
There is limited data with regard to the available paediatric surgical workforce in South Africa as well as their employment prospects upon completion of their specialisation training. These data are essential in developing a National Surgical Plan to address the burden of surgical disease as well as determining where resource allocation is needed. In addition, specialist paediatric surgeons who are unable to find suitable employment are more likely to emigrate, leading to further collapse of the surgical health care system. This aim of this study was to quantify and analyse the paediatric surgical workforce in South Africa as well as to determine their geographic and sector distribution. This research builds on previous research conducted in the field of general surgery and continues to grow the national database on surgical resource in South Africa. This study involved a quantitative descriptive analysis of all registered specialist as well as training paediatric surgeons in South Africa, and included their demographic data, the geographic location of their practice, as well as the sector in which they work. Quantitative data included their plans for public, private or dual practice once they have completed their specialization training. The results showed 2.6 paediatric surgeons per one million population under 14 years. More than half (69%) were male and the median age was 46.8 years. There were however, more female surgical registrars currently in training. The majority of the paediatric surgical practitioners were found in Gauteng (43%), followed by the Western Cape (26%) and Kwa-Zulu Natal (16%). The majority of specialists reportedly worked in the public sector (40.9%), however this number may have been over-reported as hours spent in public practice were not specified. Interprovincial differences as well as intersectoral differences were marked indicating geographic and socioeconomic maldistribution of paediatric surgeons. The public sector paediatric surgeon density (per million population under 14 years) was 2.4 which fell below the private sector paediatric surgeon density of 9.4. These numbers fell far below developed countries such as the United States, Germany and the Netherlands but the private sector density compared favourably with Ireland and Canada. Access to paediatric surgical care requires an adequate supply of experienced surgeons distributed over a wide geographical area. Additionally, paediatric surgeons require a wide range of ancillary support staff and hospital facilities. Without these resources, surgical access for the most vulnerable of populations is limited. Addressing the maldistribution of paediatric surgical workforce requires concerted efforts to expand existing training posts as well as equipping the remainder of level three hospitals to provide paediatric surgical training.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".