Comparison of the Collection Status of Unused Medicines Domestically and Internationally and Derivation of Policy Implications
Bibliographic record
Abstract
Improper disposal of unused or expired medications poses significant risks to public health and the environment. In South Korea, pharmaceutical waste management is regulated at the municipal level through local ordinances, resulting in inconsistent practices and fragmented regulations. This study compared South Korea’s system with those of the United States, Canada, and Australia, where federal or state laws ensure more systematic and uniform management. In 2021, South Korea’s per capita pharmaceutical waste collection rate (0.008 kg) was lower than Canada’s (0.0123 kg) and Australia’s (0.031 kg). Among South Korean regions, Sejong Special Self-Governing City achieved the highest per capita collection rate (0.0388 kg), likely due to an efficient collection process. The study identified key challenges, including low public awareness, inadequate promotion of proper disposal practices, and the absence of an integrated regulatory framework. To overcome these challenges, this study suggests implementing unified national regulations, developing systematic programs, and strengthening collaboration among key stakeholders. These measures are anticipated to enhance the efficiency of pharmaceutical waste collection systems in South Korea, providing a foundation for public health promotion and environmental protection.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".