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Record W2332904423 · doi:10.20286/nova-jmbs-030234

Medicinal Plants Used in Paediatric Health Care in Namungalwe Sub County, Iganga District, Uganda

2014· article· en· W2332904423 on OpenAlexvenueno aff
Patricia A. Nalumansi, Maud Kamatenesi‐Mugisha, Anywar Godwin

Bibliographic record

VenueNova Journal of Medical and Biological Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGeographyTraditional medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.339
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2014
Admission routes1
Has abstractyes

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