Bibliographic record
Abstract
Background: At the end of the‘Waste Drug Disposal Project’, collection and disposal of waste drugs remain a social issue. Objective: This study aimed to provide suggestions to improve the drug waste management system in Korea by comparing domestic and overseas relevant programs. Methods: This is a comparative study between South Korea, Australia, Canada, France, and the US. These overseas countries were selected because they have been operating waste drug management programs continuously to date. Comparison was conducted by a pre-determined analysis frame including legal regulation, enforcement program and its performance. Results: All selected countries except Australia had legal regulations on drug wastes. The US had the largest variety of drug waste disposal methods. Canada had recommended that pharmacies actively participate in drug waste withdrawal programs. France had the largest variety of methods to promote relevant programs, including window sticker, SNS, and app, as well as the highest level of awareness and participation. Australia had the lowest level of awareness and participation in pharmaceutical waste management programs. Pharmaceutical companies took responsibility of paying for these programs in the selected overseas countries. Conclusion: Further efforts should be made to establish a clear guideline including the role of pharmaceutical companies, and to develop various methods for the public to be aware of appropriate ways of disposing drug wastes in Korea.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".