Knowledge and perception of tuberculosis and the risk to become treatment default among newly diagnosed pulmonary tuberculosis patients treated in primary health care, East Nusa Tenggara: a retrospective study
Bibliographic record
Abstract
BACKGROUND: Despite the high efficacy of tuberculosis (TB) drug regiments, one of the barriers in the TB control program is the non-compliance to treatment. Morbidity, mortality, and risk to become resistant to drugs are emerging among defaulters. Thus, the aim of this study is to identify the factors, especially knowledge and perceptions of TB and association with treatment default among patients treated in primary care settings, East Nusa Tenggara. METHODS: This study was part of a bigger cohort community-based controlled trial study. The subjects were newly diagnosed pulmonary TB patients from four districts in East Nusa Tenggara. Knowledge, perception of TB, and other related factors were assessed prior to the treatment. Patients who interrupted the treatment in two consecutive months were classified as defaulters, as World Health Organization stated. Odds ratio (OR) looking for factors associated with becoming defaulter was analyzed. RESULTS: A total of 300 patients were recruited for this study. At the end of the treatment, 255 patients (85%) completed the treatment without interruption from regular visit. In univariate analysis, none of the socio-demographic factors attributed to treatment default yet lack of knowledge and incorrect perception of TB prior therapy (OR 2.49 1.30-4.79 95% CI, p = 0.006; OR 5.40 2.64-11.04 95% CI, p < 0.001, respectively). In multivariate analysis, only incorrect perception of TB showed significant association with treatment default (OR 4.75 2.30-9.86 95% CI). CONCLUSIONS: Assessing the knowledge and perception of TB prior to the treatment in newly pulmonary TB patients is important as both of them were known as risk factor for treatment default. Education and counseling may be required to improve patients' compliance to treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".