Caregivers’ treatment-seeking behaviors and practices in Uganda—A systematic review (Part II)
Bibliographic record
Abstract
Prompt treatment with artemisinin combination therapies (ACTs) remains the cornerstone for managing uncomplicated malaria caused by Plasmodium falciparum. In accordance with global initiatives to curb malaria, the Ugandan government pledged to increase the proportion of children under five to receive the first-line antimalarial treatment to 85% by 2010. To achieve this, Uganda has implemented several initiatives to improve prompt access to ACTs. While several studies have evaluated various aspects of caregivers' treatment-seeking and its impact on malaria management in children since the advent of ACT, this is the first systematic review to synthesize the Uganda literature since 2004. A comprehensive search employed key web search engines databases. A total of 19 studies met the inclusion criteria. This review found that although most Ugandans associate mosquitoes with malaria, misconceptions about the cause of malaria is common. Home management continues to be a common first response, with most caregivers seeking subsequent treatment if the child does not improve. A major concern arising from this review was that an increase in ACT knowledge does not always translate into a child receiving an ACT. While 84% of caregivers in a recent national study spontaneously named ACT as the antimalarial of choice, only half of the children were reported to have received an antimalarial and only 44% received an ACT. As with ACT usage, prompt use of ACT fell significantly short of the 2010 target. Given the on-going popularity of home management, future research is needed to examine the diversity and adequacy of home management for malaria and the sequence of care provided from the moment of recognition of initial symptoms. Considering the pervasive use of polypharmacy to manage malaria, further research is needed to quantify the proportion of children who receive an appropriate antimalarial - defined as receiving only the first-line treatment. Finally, given children's reliance on caregivers to seek out appropriate care on their behalf, future studies need to examine specific assets and challenges influencing caregivers' treatment-seeking behaviors in order to positively influence decision-making at the household level.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".