The Community Disease Prevention Behaviors in District Maros South Sulawesi Province
Bibliographic record
Abstract
The community diseases prevention behaviors assumed influenced by knowledge of infectious disease, hygiene and health knowledge, motivation and of behaviors aof disese prevention than influence by attitude prevention of infectious diseases. This study aimed to examine the effect of variable knowledge infectious disease, hygiene and health knowledge, motivation prevention of infectious diseases, an attitude towards the behavior of infectious disease prevention. The research was conducted in Maros Regency, South Sulawesi Province with a quantitative approach survey method, which uses the quesioner that measure all variables mentioned above. The population in this study is the district's communities sampled from the Turikale, Mandai and Bantimurung sub-District of 200 respondents. The Structural Equational Modeling (SEM) is used to assess significant relation between all variable. The results showed that infectious disease prevention knowledge, attitudes prevention of infectious diseases affect the community behavior of infectious disease prevention, while knowledge of hygiene and health, communicable disease prevention motivation does not affect the behavior of infectious disease prevention. These results, suggest that knowledge of hygiene and health and prevention of infectious diseases motivation of concern that the incidence of infectious diseases, especially pulmonary tuberculosis, dengue fever, and diarrhea in Maros is not increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".