The paediatrician workforce and its role in addressing neonatal, child and adolescent healthcare in Kenya
Bibliographic record
Abstract
OBJECTIVE: To examine the availability of paediatricians in Kenya and plans for their development. DESIGN: Review of policies and data from multiple sources combined with local expert insight. SETTING: Kenya with a focus on the public, non-tertiary care sector as an example of a low-income and middle-income country aiming to improve the survival and long-term health of newborns, children and adolescents. RESULTS: There are 305 practising paediatricians, 1.33 per 100 000 individuals of the population aged <19 years which in total numbers approximately 25 million. Only 94 are in public sector, non-tertiary county hospitals. There is either no paediatrician at all or only one paediatrician in 21/47 Kenyan counties that are home to over a quarter of a million under 19 years of age. Government policy is to achieve employment of 1416 paediatricians in the public sector by 2030, however this remains aspirational as there is no comprehensive training or financing plan to reach this target and health workforce recruitment, financing and management is now devolved to 47 counties. The vast majority of paediatric care is therefore provided by non-specialist healthcare workers. DISCUSSION: The scale of the paediatric workforce challenge seriously undermines the ability of the Kenyan health system to deliver on the emerging survive, thrive and transform agenda that encompasses more complex health needs. Addressing this challenge may require innovative workforce solutions such as task-sharing, these may in turn require the role of paediatricians to be redefined. Professional paediatric communities in countries like Kenya could play a leadership role in developing such solutions.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".