Exploring the Complexities of Leprosy-related Stigma and the Potential of a Socio-economic Intervention in a Public Health Context in Indonesia
Bibliographic record
Abstract
Purpose: This article explores the complexities of leprosy-related stigma and the potential effectiveness of a socio-economic intervention in Cirebon District, Indonesia. Methods: A qualitative approach was adopted. 53 people affected by leprosy were interviewed, and 17 focus group discussions were conducted among people affected by leprosy, community and religious leaders, and health providers and other key persons who were all purposively selected. Results: People affected by leprosy face major socio-economic consequences. This was confirmed by key persons. Several opportunities for a possible socio-economic intervention were perceived, as also the barriers. People affected by leprosy are constrained by certain aspects of the health system (e.g., the health providers’ negative attitudes), views in society (e.g., misunderstandings about the condition, stigma), and the physical and social consequences of the disease (impairments, feelings of shame). Study participants identified strategies to deal with these barriers, as well as specific activities for a socio-economic intervention; in particular, the training of staff responsible for implementation. Conclusion and Implications: Socio-economic interventions in the field of leprosy need to anticipate the barriers and develop strategies to deal with them. Cooperation between people working in the health system and those in the welfare / financial system is needed, to improve the quality of life of people affected by leprosy
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".