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Record W4401920675 · doi:10.3390/plants13172383

Survey on the Traditional Use of Medicinal Herbs in Haiti: A Study on Knowledge, Practices, and Efficacy Prevention

2024· article· en· W4401920675 on OpenAlexfundno aff
Valendy Thesnor, Yvens Chérémond, Muriel Sylvestre, Patrick Meffre, Gerardo Cebrián‐Torrejón, Zohra Benfodda

Bibliographic record

VenuePlants · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsTraditional medicineDecoctionMedicineMedicinal herbsMedicinal plants

Abstract

fetched live from OpenAlex

The use of medicinal herbs is highly developed in Haiti. However, there is a significant lack of knowledge in the literature on medicinal plants and their uses. The objective of this study was to determine the knowledge and practices of Haitian families for the prevention/treatment of COVID-19, influenza, and respiratory diseases, as well as the mode of preparation and administration of the plants. Individuals were interviewed using the TRAMIL questionnaire as the information holder. The data obtained were analyzed by calculating 5 indices (relative frequency of citation, use value, the family use value, informant consensus factor, and fidelity level). The study surveyed 120 Haitians and collected 75 plants from 43 botanical families. The botanical family most used for all these preventions and remedies is the Lamiaceae. The highest ranked species with a relative frequency of citation value > 0.3. Infusion, decoction, and in the form of punch are the methods used for the remedies. The study found that the use of herbal remedies is still prevalent in the study area, and many of the commonly used plants have been scientifically validated. However, some plants, such as Samyda rosea Sims, lack sufficient research and are recommended for further investigation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.325
GPT teacher head0.359
Teacher spread0.033 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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