The role of traditional and faith healers in the treatment of dementia in Tanzania and the potential for collaboration with allopathic healthcare services: Table 1.
Bibliographic record
Abstract
Background: Low diagnostic rates are a barrier to improving care for the growing number of people with dementia in sub-Saharan Africa. Many people with dementia are thought to visit traditional healers (THs) and Christian faith healers (FHs) and these groups may have a role in identifying people with dementia. We aimed to explore the practice and attitudes of these healers regarding dementia in rural Tanzania and investigate attitudes of their patients and their patients’ carers. Methods: This was a qualitative study conducted in Hai district, Tanzania. Semi-structured interviews were conducted with a convenience sample of THs and FHs and a purposive-stratified sample of people with dementia and their carers. Interview guides were devised which included case vignettes. Transcripts of interviews were subject to thematic analysis. Findings: Eleven THs, 10 FHs, 18 people with dementia and 17 carers were recruited. Three themes emerged: (i) conceptualisation of dementia by healers as a normal part of the ageing process and no recognition of dementia as a specific condition; (ii) people with dementia and carer reasons for seeking help and experiences of treatment and the role of prayers, plants and witchcraft in diagnosis and treatment; (iii) willingness to collaborate with allopathic healthcare services. FHs and people with dementia expressed concerns about any collaboration with THs. Conclusions: Although THs and FHs do not appear to view dementia as a specific disease, they may provide a means of identifying people with dementia in this setting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".