Ethnobotanical Study of Medicinal Plants Used against COVID-19
Bibliographic record
Abstract
During the COVID-19 pandemic, the Moroccan population, like the entire population of the world, used medicinal plants to treat or cure symptoms of SARS-CoV-2. The present work was designed to identify the medicinal plants used by the Moroccan population in the prevention or treatment of COVID-19. To achieve this goal, a survey was conducted to collect data on plants along with the sociodemographic parameters of users. The outcome of this work showed that 1,263 people were interviewed with 63.5% male, aged between 18 and 82 years. Most plant users were between 20 and 40 years, which constituted 80.1% of the study population. The level of education of participants was 70.9% university and 27.6% secondary. The most useful plants were eucalyptus, cloves, lemon, and garlic. Notably, 61.9% of interviewed people used plants for preventing or treating COVID-19: 30.6% of them declared one-time use from the beginning of the pandemic, and 47.8% declared frequent daily use until recovery, while 17.4% declared single daily use. Five out of twenty-one plants used in the treatment are known for their potential toxicity, including Artemisia herba-alba and oleander (Nerium oleander). The findings of the present work could serve society by providing potential medicinal plants to control COVID-19.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".