Challenges with accessing health care for young children presumed to have malaria in the rural district of Butaleja, Uganda: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: A qualitative study was conducted to gain insight into challenges reported by Butaleja households during a previous household survey. Specifically, this paper discusses heads of households' and caregivers' perceptions of challenges they face when seeking care for their very young children with fever presumed to be malaria. METHODS: Eleven focus groups (FGs) were carried out with household members (five with heads of households and six with household caregivers) residing in five sub-counties located across the district. Purposive sampling was used to ensure the sample represented the religious diversity and geographical distance from the peri-urban center of the district. Each FG consisted of five to six participants. The FGs were conducted at a community centre by two pairs of researchers residing in the district and who were fluent in both English and the local dialect of Lunyole. The discussions were recorded, translated, and transcribed. Transcripts were reviewed and coded with the assistance of QDA Miner (version 4.0) qualitative data management software, and analyzed using thematic content analysis. RESULTS: The FG discussions identified four major areas of challenges when managing acute febrile illness in their child under the age of five with presumed malaria (1) difficulties with getting to public health facilities due to long geographical distances and lack of affordable transportation; (2) poor service once at a public health facility, including denial of care, delay in treatment, and negative experiences with the staff; (3) difficulties with managing the child's illness at home, including challenges with keeping home-stock medicines and administering medicines as prescribed; and (4) constrained to use private outlets despite their shortcomings. CONCLUSIONS: Future interventions may need to look beyond the public health system to improve case management of childhood malaria at the community level in rural districts such as Butaleja. Given the difficulties with accessing quality private health outlets, there is a need to partner with the private sector to explore feasible models of community-based health insurance programs and expand the role of informal private providers.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".