Lymphatic filariasis as an indigenous illness: the local context of a global disease
Bibliographic record
Abstract
Lymphatic filariasis (LF), also known as elephantiasis, is a chronic disease that is devastating for patients, both bodily and socially. Besides the unbearable chronic pain, disfigured limbs, and loss of livelihood, people living with LF are stigmatized and sometimes abandoned by family. To ease the burden of people living with LF, known locally as gyepim or duba , the social and cultural aspects must be investigated in unison with the biomedical strategies already in place. As part of a multi-year pilot study in the Western Region of Ghana, funded by Canadian Institutes for Health Research, this article investigates the ways that traditional healers man the stigma associated with LF. Interviews were conducted in the local languages to collect data. These were transcribed and translated to English for thematic and content analysis. Preliminary findings indicate that the traditional healers of the Western Region of Ghana regard the illness within an indigenous worldview an ecology and history borne of the spiritual topography of Western Ghana. Healers, known as okomfoi , are conversant in the causes and outcomes of LF, and can offer varied diagnoses and cures for the disease within an indigenous framework. Additionally, they claim to do so without the need to antagonize allopathic practitioners. These insights demonstrate that thinking about LF as an indigenous ailment can offer new understandings of how we might eliminate this neglected tropical disease.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".