Knowledge, attitude, and practices toward malaria among hospital outpatients in Nangarhar, Afghanistan: A cross-sectional study
Bibliographic record
Abstract
Background: In the Eastern Mediterranean region, Afghanistan ranks third for the world’s highest burden of malaria. The vast majority (95%) of malaria cases in Afghanistan are attributed to Plasmodium falciparum and 5% to Plasmodium vivax. Most cases occur in low-altitude regions, especially in the eastern province of Nangarhar, where agriculture and farming are predominant. To better understand the public sentiment toward malaria, this study aimed to understand the knowledge, attitude, and practice of patients toward malaria who visited public and private hospitals of Nangarhar province. Methods: A cross-sectional descriptive study was conducted on Nangarhar residents who visited the adult Outpatient departments of eight local public and private health facilities. Data collection took place from 1st August 2022 to 15th September 2022. Results: Of 700 participants, 37.9% ( n = 265) identified as male and 62.1% ( n = 435) identified as female. The majority of participants (84.6 %) were within the (18–40) age range, followed by 12.7% in the (41–60) age range, and 2.7% were aged 61 years or older. Moreover, 99.7% ( n = 698) of the participants had heard of malaria. The main sources of information about malaria were family members (31.3%, n = 219), television (32.6%, n = 228), Internet (12.6%, n = 88), school (11.3%, n = 79), and health facilities (31.4%, n = 220). Most respondents correctly identified mosquito bites as the primary mode of malaria transmission (72.6%, n = 508). Others suggested that transmission could occur by close contact with a malaria patient (14.0%, n = 98) and drinking contaminated water (17.3%, n = 121). The majority of participants (70.6%) agreed that malaria is a serious and life-threatening disease. A significant number of participants (96.6%) reported owning an insecticide-treated mosquito net at home, and 87.0% reported using the net. Conclusion: Overall, participants reported good knowledge, attitude, and practice toward malaria. This may be linked to the awareness campaigns and preventive programs in Nangarhar province that have contributed to participant’s willingness to prevent malaria and treat themselves if they get infected. Public health campaigns are difficult in Afghanistan with weak governance and conflict, and thus, populations may find themselves at risk if health promotion activities are stopped.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".