Bridging gaps: a qualitative inquiry on improving paediatric rheumatology care among healthcare workers in Kenya
Bibliographic record
Abstract
Abstract Background Due to the paucity of paediatric rheumatologists in Kenya, it is paramount that we explore strategies to bridge clinical care gaps for paediatric rheumatology patients in order to promote early diagnosis, prompt referral, and optimal management. Purpose To identify proposed interventions which can improve the ability of non-specialist healthcare workers to care for paediatric rheumatology patients across Kenya. Methods We conducted 12 focus group discussions with clinical officers (community physician assistants), nurses, general practitioners and paediatricians across six regions in Kenya. Interviews were conducted, audio-recorded, transcribed verbatim, and analysed using MAXQDA 2022.2 software. Results A total of 68 individuals participated in the study; 11 clinical officers, 12 nurses, 10 general practitioners, 27 paediatricians and eight other healthcare workers. Proposed patient interventions included patient education and psychosocial support. Community interventions were outreach awareness campaigns, mobilising financial support for patients’ care, mobilising patients to access diagnostic and therapeutic interventions. Healthcare worker interventions include diagnostic, management, and referral guidelines, as well as research and educational interventions related to symptom identification, therapeutic strategies, and effective patient communication skills. In addition, it was highlighted that healthcare systems should be bolstered to improve insurance coverage and access to integrated multi-disciplinary clinical care. Conclusions Study participants were able to identify potential initiatives to improve paediatric rheumatology care in Kenya. Additional efforts are underway to design, implement and monitor the impact of some of these potential interventions,
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".