Community-based models for neglected tropical diseases affecting the skin: a scoping review
Bibliographic record
Abstract
Skin neglected tropical diseases (NTDs) cause physical impairments, and socioeconomic and mental health impacts, often exacerbated by social stigma. The WHO 2030 road map calls for integrated, person-centred care; community-based groups (CBGs) present a community-led solution. However, evidence is limited and CBGs are not consistently being operationalised across health systems. The UPLIFT study is developing and piloting a harmonised CBG tool. This scoping review aimed to identify preferred practices in existing skin-NTD CBG models and recommend a holistic framework for best practice. We searched four peer-reviewed databases and key organisations’ websites, for terms related to CBGs and skin-NTDs. Eligible studies explored community models and skin NTDs and were published in English from 2000 onwards. Studies with quantitative, qualitative, or mixed method approaches, and secondary analysis or evidence synthesis were eligible. Studies failing to fulfil the criteria, opinion pieces, cross-sectional surveys, editorials, and case studies were excluded. Outcomes on key CBG domains were thematically identified and explored from literature and discussions with subject matter experts: self-care, mental health, livelihood, and governance. Data were compiled in Excel and charted according to CBG domain and other key information (e.g., outcome and method of measurement), before writing up findings thematically. Fifty-four eligible citations were identified, including 38 peer-reviewed papers. Most studies were pretest/posttests conducted in Asia, targeting mainly leprosy, with heterogeneity in study designs and evaluative measures. Only four studies explored all CBG domains, with few CBGs including formal mental health support, livelihood activities and advocacy and awareness activities. Current research on the burden of skin-NTDs is predominantly focused on leprosy, leaving a significant gap in research on other skin-NTDs, particularly in the African region. Most CBGs are failing to integrate into health systems and mainstream important programmatic components such as sustainability and gender considerations. To address these gaps and work towards a harmonised CBG approach, we present a framework of best practice and recommend further research, targeting multiple skin-NTDs in unison.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".