Risk Factors of Physical Condition of House and Clean and Healthy Living Behavior (PHBS) to Tuberculosis in Kaluku Bodoa Health Center Area, Makassar City
Bibliographic record
Abstract
Background: Tuberculosis remains the 10th leading cause of death globally, accounting for approximately 1.3 million fatalities. The physical conditions of a house, including ventilation, humidity, temperature, occupancy density, lighting, and Clean and Healthy Living Behavior (PHBS), are crucial factors that should be considered in relation to TB incidence. Objective: This study aims to analyze the relationship between house physical conditions and PHBS with the incidence of TB in the working area of the Kaluku Bodoa Public Health Center, Makassar City. Methods: This study employed an observational analytic design with a cross-sectional approach. The sample size for the study comprised 150 respondents. Data were processed using univariate analysis, presented in tables, and further analyzed descriptively and bivariately using the Chi-square test to determine the relationship between house physical conditions and CHLB with TB incidence in the working area of the Kaluku Bodoa Public Health Center, Makassar City. Results: There was a significant relationship between ventilation, lighting, occupancy density, and PHBS with TB incidence in the Kaluku Bodoa Public Health Center, Makassar City. At the same time, temperature and humidity were found to have an insignificant effect on TB incidence. Conclusion: The findings of this study can be used to guide government policies aimed at improving the quality of life for individuals with TB. Environmental health officers can implement intensive programs emphasizing the importance of handwashing, maintaining cleanliness, and ensuring proper ventilation to reduce the risk of TB transmission.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".