Exploring Barriers and Facilitators of Adherence to Artemisinin-Based Combination Therapies for the Treatment of Uncomplicated Malaria in Children in Freetown, Sierra Leone
Bibliographic record
Abstract
Medication adherence is an essential step in the malaria treatment cascade. We conducted a qualitative study embedded within a randomized controlled trial comparing the adherence to the recommended dosing of two artemisinin-based combination therapies (ACT) to treat uncomplicated malaria in Freetown, Sierra Leone. This study explored the circumstances and factors that influenced caregiver adherence to the ACT prescribed for their child in the trial. In-depth interviews were conducted with 49 caregivers; all interviews were recorded, transcribed, and translated. Transcripts were coded and aggregated into themes, applying a thematic content approach. We identified four key factors that influenced optimal treatment adherence: (1) health system influences, (2) health services, (3) caregivers' experiences with malaria illness and treatment, and (4) medication characteristics. Specifically, caregivers reported confidence in the health system as facilities were well maintained and care was free. They also felt that health workers provided quality care, leading them to trust the health workers and believe the test results. Ease of medication administration and perceived risk of side effects coupled with caregivers' prior experience treating malaria influenced how medications were administered. To ensure ACTs achieve maximum effectiveness, consideration of these contextual factors and further development of child-friendly antimalarials are needed.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".