Integrating Traditional Medicine and Healing into the Ghanaian Mainstream Health System: Voices From Within
Bibliographic record
Abstract
In this study, I employed interpretive ethnographic qualitative design to explore perceptions of and proposals from traditional healers, biomedical practitioners, and health care consumers regarding integrating traditional medicine and healing in Ghana. Data were gathered through focus groups, in-depth individual interviews, and qualitative questionnaires and analyzed thematically. The results revealed positive attitudes toward integrating traditional medicine in Ghana and a discursive discourse of power relations. The power imbalance between biomedical and traditional practitioners regarding what integrative models to adopt is sanctioned by formal education and institutional structure. As a result, multiple approaches for integration were made, including patient co-referrals, collaborations between biomedical and traditional medical practitioners, and creating a unit for traditional medicine and healers at the outpatients' department for patients to choose either biomedicine or traditional medicine. Incorporating aspects of traditional healing in the training of biomedical practitioners and creating a space for knowledge sharing were also proposed. These integrative models reflected the distinctive interests of healers and biomedical practitioners. Considering these findings, I recommended policy options for consideration toward achieving an integrative health care system in Ghana.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".