Predictors of traditional medical knowledge transmission and acquisition in South West Nigeria
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".