Knowledge, beliefs and adherence to antimalarial medications among patients in the Ga East Municipality of Ghana
Bibliographic record
Abstract
Treatment adherence is necessary for several reasons, including preventing the development of resistance and avoiding progression to a severe form of the disease. This study explored patients’ beliefs and their impact on adherence to antimalarial medications (orthodox and herbal medicines). A cross-sectional survey was conducted over three weeks in the Ga East Municipal District of Ghana. The study involved 346 participants and employed comprehensive questionnaires to gather data, which was analyzed using STATA version 14. Despite the high prevalence of malaria in the district, adherence to antimalarial medication remained a challenge. The study revealed varying opinions among participants regarding the safety and efficacy of different antimalarial treatments. The research highlighted the connection between patients’ beliefs, age, knowledge, and adherence. Younger individuals showed higher adherence rates, emphasizing the need for age-specific interventions. Comprehensive knowledge also correlated with better adherence, highlighting the role of education. The study recommended the development of tailored educational campaigns that address misconceptions and foster trust between healthcare providers and patients. Innovative interventions, such as mobile phone-based reminders and incentives, were suggested to improve adherence. The research emphasized the importance of holistic malaria prevention strategies, including robust educational initiatives, targeted interventions in hotspot districts, and widespread distribution of insecticide-treated nets. The study’s insights offer actionable recommendations to enhance malaria control efforts in the Ga East Municipal District and beyond.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".