FACTORS INFLUENCING MALARIA TREATMENT AND PATIENT ADHERENCE TO ANTIMALARIAL DRUGS IN SOUTHERN ETHIOPIA
Bibliographic record
Abstract
In Ethiopia the health system is underdeveloped and much of the rural population has limited access to modern health services. The Ethiopian government introduced the Health Extension Program which is a community-based health care delivery system aimed at accessing essential health services such as malaria treatment through its health extension workers (HEWs). The objective of this study was to evaluate factors influencing malaria treatment practice of health extension workers (HEWs) and patient adherence to antimalarial drugs. A qualitative research design that is explorative and descriptive was used. Data was collected by means of (1) in-depth individual interviews among 20 HEWs, and seven focus group discussions with malaria treated patients. Data was analyzed thematically. Four themes emerged from the data, namely: (1) health facility related factors, (2) HEWs related factors, (3) patient related factors, and (4) community related factors. Improving the availability of essential resources such as rapid diagnostic test kits (RDT) and antimalarial drugs, improving the community’s perception towards antimalarial drug effectiveness and adequately educating patients on how to take antimalarial drugs can improve malaria treatment practice of the HEWs and patient adherence to antimalarial drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".