Risk Factors of Pulmonary Tuberculosis in the Working Area of Perumnas Public Health Center Kendari City
Bibliographic record
Abstract
Kendari City is the area with the highest number of TB cases in Southeast Sulawesi Province with a total of 488 cases in 2019. Preliminary data at the Perumnas Public Health Center showed that there were 49 TB cases in 2019. This study aims to determine the risk factors for the incidence of pulmonary TB in the Work Area. Public Health Center. This study uses a case control design. The study population was 105 patients, with a sample consisting of 44 case samples and 44 control samples, which were taken by simple random sampling. Data analysis using Chi Square test and Odds Ratio. From the results of the study, it was found that there were significant risk factors between smoking habits (OR = 5,156), contact history (OR = 8,333), occupancy density (OR = 2,544), knowledge (OR = 3,852) and ventilation (OR = 3,071) with the incidence of pulmonary tuberkulosis. The conclusion of this study is smoking habits, contact history, occupancy density, knowledge, and ventilation are risk factors for the incidence of pulmonary tuberkulosis at the Perumnas Public Health Center. Therefore, it is suggested to the health workers of the Puskesmas are expected to continue to provide health promotion and improve work programs related to pulmonary TB in order to increase knowledge and awareness of the community to prevent transmission of pulmonary TB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".