AWARENESS, PATTERNS, AND ASSOCIATED RISKS OF OVER-THE-COUNTER (OTC) DRUG USE IN THE GENERAL POPULATION OF NORTH AMERICA
Bibliographic record
Abstract
Background: Over the counter (OTC) medications are widely used across North America, contributing significantly to self-care, public health and for self-treatment among the general population all around the world. Although OTC medications are generally considered safe and effective, they carry risks of misusing of drugs by youth, adverse drug reactions (ADR) by elderly and drug interactions or Fetal risk during pregnancy particularly more in vulnerable populations/areas. This review explores the patterns and behaviors associated with OTC drug use with a focus on population demographics, common product categories, safety concerns with awareness of drug usage risks among the general population mostly highlighting the youth, elderly people and pregnant women’s in the United States and Canada.[2,3] Using publicly available datasets and simulated Power BI visualizations, the analysis highlights key differences in usage trends and identifies potential risks among vulnerable groups such as the elderly and pregnant women. Findings emphasize the need for improved medication literacy, pharmacist involvement, and ongoing monitoring of OTC consumption patterns. Conclusion: While OTC medications support self-care in the USA and Canada, targeted education, pharmacist guidance, and public health interventions are necessary to reduce misuse and ensure safe use, particularly among high-risk groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".