Social Marketing on Dengue Hemorrhagic Fever and Tuberculosis Prevention and Control Program in Pati, Central Java, Indonesia
Bibliographic record
Abstract
Indonesia has the highest number of dengue fever cases in Southeast Asia and the second highest TB cases in the world. Both diseases are related to behavior. Social marketing focuses on changes in health behaviors. This study aimed to apply social marketing on dengue mosquito vector control and TB case finding and to analyze the effect of social marketing training on the knowledge and skills of community health workers (CHWs). A mixed method design was conducted in Pati, Central Java, Indonesia. First, a case study was conducted using field observation, in-depth interviews, focus group discussions (FGD), and document review. In-depth interviews and FGD were conducted on 55 participants including 40 community leaders and 15 CHWs. Data were analyzed using content analysis. Second, intervention study was conducted on social marketing training of 30 CHWs. The independent variable was social marketing training. The dependent variables were knowledge and skill of dengue mosquito vector control and TB case finding. The effect of training was analyzed by paired t test. The results showed that knowledge (p<0.001) and skill (p<0.001) in dengue mosquito vector control and TB case finding increased significantly after training. Qualitative assessment showed that CHWs were more able to identify health problems in the community and to perform TB case finding and dengue mosquito breeding place eradication. After training they also became more knowledgeable in applying social marketing approach to address the health problem. In conclusion, social marketing strategy can be used to address community health problem.
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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 | 0.000 | 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) | 0.003 | 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".