Caregivers’ treatment-seeking behaviors and predictors of whether a child received an appropriate antimalarial treatment: a household survey in rural Uganda
Bibliographic record
Abstract
BACKGROUND: This study responds to a rural community's concern that, despite national initiatives, malaria management in young children falls short of national guidelines in their district. This study aimed to: (1) describe caregivers' treatment-seeking behaviors in the rural district of Butaleja, (2) estimate the percentage of children who received an appropriate antimalarial, and (3) determine factors that maximized the likelihood of receiving an appropriate antimalarial. Appropriate antimalarial in this study is defined as having received only the Uganda's age-specific first-line malaria treatment for uncomplicated and severe malaria during the course of the febrile illness. METHODS: A household survey design was used in 2011 to interview 424 caregivers with a child aged five and under who had fever within the two weeks preceding the survey. The survey evaluated factors that included: knowledge about malaria and its treatment, management practices, decision-making, and access to artemisinin combination therapy (ACT) and information sources. Bivariate analysis, followed by logistic regression, was used to determine predictors of the likelihood of receiving an appropriate antimalarial. RESULTS: Home management was the most common first action, with most children requiring a subsequent action to manage their fever. Overall, 20.9 % of children received a blood test, 68.4 % received an antimalarial, and 41.0 % received an ACT. But closer inspection showed that only 31.6 % received an appropriate antimalarial. These results confirm that ACT usage and receipt of an appropriate antimalarial in Butaleja remain well below the 2010/2015 target of 85 %. While nine survey items differentiated significantly whether a child had or had not received an appropriate antimalarial, our logistic regression model identified four items as independent predictors of likelihood that a child would receive an appropriate antimalarial: obtaining antimalarials from regulated outlets (OR = 14.99); keeping ACT in the home for future use (OR = 6.36); reporting they would select ACT given the choice (OR = 2.31); and child's age older than four months (OR = 5.67). CONCLUSIONS: Few children in Butaleja received malaria treatment in accordance with national guidelines. This study highlighted the importance of engaging the full spectrum of stakeholders in the management of malaria in young children - including licensed and unlicensed providers, caregivers, and family members.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".