Preserving traditional medical knowledge through modes of transmission: A post-positivist enquiry
Bibliographic record
Abstract
Background: In Nigeria, most rural communities lack access to orthodox medical facilities despite an expansion of orthodox health care facilities and an increase in the number of orthodox health care providers. Over 90% of Nigerians in rural areas thus depend wholly or partly on traditional medicine. This situation has led to a call for the utilisation of Traditional medical practitioners in primary-healthcare delivery. Hence, the persistence of the knowledge of traditional medicine, especially in the rural communities where it is the only means of primary health care, has been a concern to information professionals.Objectives: This study investigated the role which the mode of transmission plays in the preservation of traditional medical knowledge.Method: A post-positivist methodology was adopted. A purposive sampling technique was used to select three communities from each of the six states in South-Western Nigeria. The snowball technique was used in selecting 228 traditional medical practitioners, whilst convenience sampling was adopted in selecting 529 apprentices and 120 children who were not learning the profession. A questionnaire with a five-point Likert scale, key-informant interviews and focus-group discussions were used to collect data. The quantitative data was analysed using descriptive statistics whilst qualitative data was analysed thematically.Results: The dominant mode of knowledge transmission was found to be oblique (66.5%) whilst vertical transmission (29.3%) and horizontal transmission (4.2%) occurred much less.Conclusion: Traditional medical knowledge is at risk of being lost in the study area because most of the apprentices were children from other parents, whereas most traditional medical practitioners preferred to transmit knowledge only to their children.
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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 | 0.030 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".