Assessment of Knowledge, Attitude, and Practice Toward Tuberculosis: A Cross‐Sectional Study in Balkh, Afghanistan
Bibliographic record
Abstract
ABSTRACT Background and Aims Tuberculosis (TB) remains a major public health challenge in Afghanistan, requiring enhanced community engagement for effective control. This study assessed the knowledge, attitudes, and practices (KAP) related to TB among outpatients in Balkh to inform targeted interventions. Methods A convenience‐based, face‐to‐face cross‐sectional study was conducted from June 2024 to April 2025 on 867 hospital outpatients in Balkh using a structured questionnaire developed from peer‐reviewed articles. A pilot study with 30 participants showed Cronbach's α = 0.767. Descriptive statistics, χ 2 test, multivariable logistic regression analysis, and Spearman's correlation were performed using SPSS v.27, with statistical significance set at p < 0.05. Results Of the 867 participants, 63.7%, 52.7%, and 51.4% showed good knowledge, attitude, and practice, respectively. Additionally, good TB‐related knowledge was significantly associated with being married (OR = 6.67), university education (OR = 3.31), prior awareness of TB (OR = 2.29), history of TB treatment (OR = 2.79), and TB vaccination (OR = 1.97) (all p < 0.05). Positive attitudes were linked to being married, unskilled employment (OR = 1.83), higher income (OR = 2.50), prior TB awareness (OR = 1.69), and having a window at home (OR = 8.03). Better practice was associated with female gender (OR = 4.20), higher income (OR = 2.02), TB awareness (OR = 1.48), and windowed housing (OR = 6.48), though unvaccinated individuals showed slightly better practice (OR = 1.44). Spearman's correlations showed significant positive associations between KAP scores (all p < 0.001). Conclusion Significant gaps in TB KAP in Balkh reflect socioeconomic and systemic barriers. Targeted education and community‐based interventions are essential for effective TB control.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".