Diverse Information Sources and The Community’s High Level of Knowledge About Lymphatic Filariasis in Air Salobar and Waihaong, Ambon City, Indonesia
Bibliographic record
Abstract
Background: Mass Drug Administration (MDA) is a strategy to eliminate lymphatic filariasis (LF) in endemic areas. However, individuals' decision to take LF drugs in MDA is associated with their knowledge and awareness about LF. This study examined the association between the community’s level of knowledge and awareness about LF with the number of informant types and media types for LF information in Waihaong and Air Salobar Health Centers, Ambon City, Indonesia. Methodology: We used data from a household survey conducted in January 2019 involving 944 respondents aged 18-70 living in the study sites. Data analysis was performed using multivariable logistic regression. Results: We found that only 33.3% of respondents had a high level of knowledge and awareness about LF. An increased odds of having a high level of knowledge and awareness about LF was associated with respondents receiving information from more than one type of informant and one type of media (aOR=10.55, 95%CI: 2.35-47.37, p=0.002), and among female respondents (aOR=1.92, 95%CI: 1.25-2.94). Conclusions: These findings emphasize the importance of comprehensive health promotion strategies using different types of informants and media to enhance the community's knowledge and awareness about LF, which is important to support the elimination of LF in Ambon City, Indonesia.
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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 | 0.003 |
| 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 | 0.000 | 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".