Co-developing strategies for leprosy management in Malaysia: A transdisciplinary research approach involving individuals affected by leprosy and other stakeholders
Bibliographic record
Abstract
Despite Malaysia's low leprosy prevalence, new cases continue to emerge in rural and Indigenous communities. National strategies often prioritise elimination and surveillance, while overlooking the needs and challenges of individuals affected by leprosy and those managing their care. This study employed a transdisciplinary research approach, guided by the Dialogue Model, to co-develop context-specific strategies to improve the lives of those affected through participatory engagement with both groups. Conducted across three high-burden states in Peninsular Malaysia, the research involved a desk review and stakeholder mapping, followed by 40 in-depth interviews with affected individuals and other stakeholders. These were complemented by a stakeholder workshop, a focus group discussion, and an evaluation of implemented strategies. Participants identified overlapping and divergent concerns, including stigma, financial hardship, access barriers, interagency coordination, and awareness gaps. These findings informed the co-development of locally grounded strategies, some of which have since been trialled at the community level. While not primarily intended to influence national policy, the study generated actionable strategies to improve leprosy care delivery in underserved settings. This approach demonstrates how transdisciplinary methods can help align community experiences with service provision in neglected tropical disease programmes, particularly in low-endemic contexts, and contribute to more equitable global health systems.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".