Medicinal Plants Used in Paediatric Health Care in Namungalwe Sub County, Iganga District, Uganda
Bibliographic record
Abstract
Background: An ethnobotanical study was carried out in Namungalwe Sub County, Iganga District Eastern Uganda, to document medicinal plant species used in disease management among children. Methods: Ethnobotanical data was collected through interviews with households and key informants, Focus Group Discussions and the Snow ball technique. This was complemented by field observations and photography. Results: A total of 61 plant species and one mushroom species, Termitomyces microcarpus were reported to be used as medicinal plants used in the disease management among children. These species belonged to 36 families and 58 genera. The most commonly mentioned medicinal plant species were Vernonia amygdalina Delile , Chenopodium opulifolium Schrad. ex W.D.J.Koch & Ziz and Albizia corialia (Schum. & Thonn.) Benth. Most of the medicinal plant species belonged to the family Leguminosae (29.7%).The most commonly used plant life forms for peadiatric health care were herbs (45.2%), and leaves (53.1%) were the most used plant parts. Most of the medicines were prepared as decoctions. Malaria and diarhoea were the most frequently occurring ailment among children. Conclusion: There is diversity of traditional knowledge on medicinal plants used in the management of ailments among children in the study area. Mothers and other care takers in homes are the custodians of this knowledge.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".