“Healing is having faith in Allah, the healer, and the medicine”: An exploratory qualitative study of Islamic-based healing practices in Northern Ghana
Bibliographic record
Abstract
Faith-based healing practices are becoming widespread in Ghana. Charismatic churches, traditional healers, and Mallams provide healthcare services for multitudes nationwide. However, these faith-based healers' guiding philosophies and healing practices are less known. This ethnographic study explores Islamic healing, medical perspectives, therapeutic practices, and how the Dagomba traditional practices and culture influence this medical system. Data were collected from six Islamic-based healers through in-depth individual interviews using a semi-structured interview guide. Thematic analysis was employed, and results are reported narratively. The study discovered that religious and faith-based healing practices, including Islamic-based healing, are rising in Ghana. Practitioners of Islamic-based healing among the Dagomba observe that having faith in Allah and trust in His healing powers is the foundation of health. Islamic principles of faith and the Dagomba health beliefs determine these healers’ medical therapy and healing practices. The study found that rituals and sacrifices are significant healing practices with specific gendered perspectives. Spirituality is perceived as both an illness-causal factor and a healing tool. We invite further research to identify the prospects, constraints, and therapeutic possibilities Islamic healing holds for Ghana’s integrative medical system_._
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".