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Record W3132120580

국내외 제도 비교를 통한 폐의약품 관리 개선 방안

2019· article· ko· W3132120580 on OpenAlexaboutno aff
김호정, 최예지, 이인향

Bibliographic record

Venue한국임상약학회지 · 2019
Typearticle
Languageko
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacy and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEnforcementVariety (cybernetics)LegislationEnvironmental planningPolitical scienceLawGeography
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.1100.037

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.

Opus teacher head0.146
GPT teacher head0.497
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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