Bibliographic record
Abstract
Objective: Online pharmacies were introduced in some countries such as United States of America or Canada. They can provide benefits to consumer because they can buy and take conveniently drugs without limitation of location or time. In Korea, online pharmacies are illegal and only pharmacists can sell drugs to consumers or patients. Therefore, we investigated the knowledge of online pharmacy and the possible problem in Korea to survey pharmacists. Methods: We developed questionnaire based on previous articles about online pharmacy and surveyed nation-wide pharmacists by mail or e-mail. The data was analyzed by SPSS and Microsoft Excel. P-values less than 0.05 were statistically significant. Results: 175 pharmacists involved in this study. About introduction of online pharmacies, 53.1% were opposition while 10.3% were approval and 36.6% were conditional. Although online pharmacies were introduced, 46.3% pharmacists do not have a plan to start online pharmacy. However, the approval and tends about starting online pharmacies were higher in younger pharmacists (20s, 30s) (p < 0.05). The criteria of permission about opening online pharmacies were 100% pharmacist license regardless of holding off-line pharmacy. 53.7% pharmacists responded education about taking medication is impossible. When online pharmacies are introduced, 65.1% pharmacists responded traditional pharmacies are affected negatively. Pharmacists concerned that the competition with large-sized distribution corporations, reduced reliance between pharmacists and patients, illegal transaction of counterfeit drugs, increased misuse of drugs. Conclusion: These results showed that Korea pharmacists have negative standard on online pharmacies. Therefore it is required to be more cautious before introducing online pharmacy and it need strict watching system and continuous education and study for safety after introducing online pharmacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".