Predictors of traditional medical knowledge transmission and acquisition in South West Nigeria
Why this work is in the frame
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Bibliographic record
Abstract
This study investigated the roles of demographic variables in the transmission and acquisition of traditional medical knowledge (TMK) in rural communities of South West Nigeria. Survey research design was adopted. Three communities from each of the six states in South West Nigeria were purposively selected. Snowball technique was used in selecting 228 Traditional Medical Practitioners (TMPs), while convenience sampling was used in selecting 529 traditional medicine apprentices. The structured questionnaire used focused on the demographic characteristics of the TMPs and their apprentices. Three key informant interviews and two focus group discussion sessions were also conducted in each state. The quantitative data were analysed using descriptive statistics, binary logistic regression and Chi square analysis, while qualitative data were analysed thematically. Logistic regression analyses showed that years of experience (Exp(B) = 1.875) was a significant predictor of knowledge transmission by the TMPs. Apprentices’ marital status (Exp(B) = 2.250), expected length of apprenticeship (Exp(B) = 0.305) and completed length of apprenticeship (Exp(B) = 15.782) were significant predictors of TMK acquisition. Qualitative results also showed a relationship between age, sex, education and TMK transmission. Enhanced level of education improved transmission, while religion reportedly hindered acquisition. Improved access to basic and adult education and the need to stop gender discrimination is recommended to improve TMK transmission.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it