Disability of patients with leprosy in Tegal Regency (Indonesia) and its association with health-seeking behavior
Bibliographic record
Abstract
Tegal Regency in Central Java Province, Indonesia, is a leprosy endemic area. In 2021, the leprosy prevalence was 0.82 per 10,000 population. The total number of new cases was 133, of whom 11% had Grade 2 disability (G2D). The high number with G2D indicates substantial case detection delay (CDD). This delay can be caused by personal, societal and health service-related factors. The aim of this study was to describe the association of disability grade with health-seeking behavior. We employed a cross-sectional research design involving 126 patients with leprosy selected by random sampling among 171 patients with leprosy registered from the 4th quarter of 2020 until the 1st quarter of 2022. Data were collected using a structured questionnaire translated from an international standardized questionnaire (ie, CDD questionnaire) through interviews with leprosy patients. Data were analyzed by descriptive and inferential statistics. We found nearly half (46.8%) of the patients aged 35-50 years, and almost two-thirds were male. Of all, 75.4% had completed primary or secondary education. At diagnosis, more than half had no disability at all, and 10.3% had G2D. Nearly half (45.2%) had used ‘self-medication’ and did not seek appropriate health care services before being diagnosed with leprosy. There was a statistically significant association between health-seeking behavior and the disability grade at diagnosis (p=0.01). In summary, disability caused by leprosy constitutes a significant public health burden in Tegal Regency, which could be lowered through supporting appropriate health seeking behavior.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".