Modeling Malaria Risk Factors by Logistic Regression Among Hilly Communities in Rural East Nusa Tenggara Province, Indonesia
Bibliographic record
Abstract
Malaria is a global health problem, including in Indonesia.Currently, the highest malaria burden is in the eastern part of the country, particularly in East Nusa Tenggara Province (ENTP).Disparities in malaria risk factors among different geographical settings are significant.However, modeling the effect of malaria knowledge levels on malaria risk factors for rural hilly communities has not been investigated yet.This study used data from 986 rural adults living in hilly areas of ENTP.Data on malaria history of participants, their various demographic, environmental and behavioral aspects of malaria were collected.Modeling was performed by using a logistic regression model.This study found that the prevalence of malaria history in hilly communities was 11.4%.The prevalence was significantly higher among those with no education (adjusted odds ratio (AOR): 2.614, 95% confidence interval (CI): 1.428-4.787)compared to those with at least a junior high school education; a low level of malaria knowledge (AOR: 2.181 with 95% CI: 1.045-4.552)compared to those with a high-level malaria knowledge; non-use of bed nets (AOR: 2.001 with 95% CI: 1.219-3.286)compared to their counterpart.Malaria health interventions and malaria knowledge modules in the local curriculum are critical to achieving the achievement of malaria elimination by 2030 in ENTP.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".