Factors Associated with Knowledge, Attitudes, and Practices about Tuberculosis in Peruvians
Bibliographic record
Abstract
Objective: To determine the factors associated with knowledge, attitudes, and practices (KAPs) about tuberculosis (TB) in the Peruvian population. Materials and Methods: A cross-sectional, analytical study was carried out by conducting a virtual survey. The instrument that was used consisted of 4 sections: sociodemographic variables (9 questions), knowledge (23 questions), attitudes (9 questions), and practices (8 questions) about tuberculosis. Univariate and bivariate analyses and the Poisson regression model with robust variance were used to obtain crude and adjusted prevalence ratios (PRa). Results: The sample consisted of 1284 participants. Regarding knowledge, attitudes, and practices about TB, an insufficient level was found in 47.97%, 50.3%, and 54.36% of the cases, respectively. The variables that increased the probability of having sufficient knowledge were sex, grade, area, family history, and history of having TB. While only the area and both antecedents were for attitudes. Finally, the age, degree, and history of TB were for the practices. Conclusion: There are insufficient KAPs in around half of the population studied. In addition, there are differences according to the epidemiological characteristics, such as sex, age, academic degree, area, and family history of TB and having had this disease. Therefore, the importance of research in this field should be emphasized in the face of a disease that is related to the differences in the levels of these variables between different strata of the general population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".