An iceberg I can’t handle: a qualitative inquiry on perceptions towards paediatric rheumatology among healthcare workers in Kenya
Bibliographic record
Abstract
BACKGROUND: Delay in diagnosis and access to specialist care is a major problem for many children and young people with rheumatic disease in sub-Saharan Africa. Most children with symptoms of rheumatic disease present to non-specialists for care. There is an urgent need to understand and scale-up paediatric rheumatology knowledge and skills amongst non-specialist healthcare workers to promote early diagnosis, prompt referral, and management. PURPOSE: We evaluated the knowledge, attitudes and practices towards diagnosis and care of paediatric rheumatology patients among health care workers in Kenya. METHODS: We conducted 12 focus group discussions with clinical officers (third-tier community health workers) nurses, general practitioners and paediatricians across 6 regions in Kenya. Interviews were conducted on zoom, audio-recorded, transcribed, and analysed using NVIVO software. RESULTS: A total of 68 individuals participated; 11 clinical officers, 12 nurses, 10 general practitioners, 27 paediatricians and 7 others. Most (n = 53) were female, and the median age was 36 years (range 31-40 years). Fifty per cent of the participants (34 of 68) worked in public health facilities. Our study revealed gaps in knowledge of paediatric rheumatology amongst healthcare workers which contributes to delayed diagnosis and poor management. Healthcare workers reported both positive and negative attitudes towards diagnosis and care of paediatric rheumatology patients. Perceived complexity and lack of knowledge in diagnosis, management and lack of health system clinical pathways made all cadres of healthcare workers feel helpless, frustrated, inadequate and incompetent to manage paediatric rheumatology patients. Positive attitudes arose from a perceived feeling that paediatric rheumatology patients pose unique challenges and learning opportunities. CONCLUSION: There is an urgent need to educate healthcare workers and improve health systems to optimize clinical care for paediatric rheumatology patients.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".