Medications and Supplements Prescription Patterns during COVID 19 Pandemic in Yemen: A Questionnaire-Based Study
Bibliographic record
Abstract
Introduction: the study aims to better understand the COVID-19 prescription treatments and over the counter regimens in Yemen in view of limited published data and limited availability of COVID-19 testing. Methods: A 34 question web-based survey was distributed on social media outlets targeting people in Yemen. Data aggregation, analysis, and visualization were performed using Tableau and Microsoft Excel. Results: 2341 individuals reported symptoms concerning for COVID-19 infection, with 25.4% reporting a chronic medical condition. Female patients were less likely to receive medications for treatment in all age groups examined. Azithromycin was the most prescription medication prescribed (32.8%) and vitamin C being the most supplement used (62%). Around 5.5% were on Hydroxychloroquine prophylaxis prior to their diagnosis and only 12.9% of them continued using after diagnosis. Conclusions: This study provides some important information about the commonly observed treatments and prescription patterns during the COVID-19 pandemic in Yemen during May- July of 2020. The study reflects the influence of global trends in medication prescription even in resource-limited countries.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".