In Whom We Trust: The Role of Certification Agencies in Online Drug Markets
Bibliographic record
Abstract
Abstract This article uses an audit sample and a consumer survey to study the intriguing market of online prescription drugs facing US customers and assesses the role that certification agencies play in online drug markets. On the supply side, we acquire samples of five popular brand-name prescription drugs from three types of online pharmacies: tier 1 are US-based and certified by the National Association of Boards of Pharmacy (NABP) or LegitScript.com, tier 2 are certified by PharmacyChecker.com or the Canadian International Pharmacy Association but not by NABP or LegitScript, and tier 3 are not certified by any of the four agencies. Most tier-2 and tier-3 websites are foreign. We find that 37 of the 365 delivered samples are different from the products we ordered and, therefore, non-testable. Conditional on testable samples, Raman spectrometry test finds no failure of authenticity except for eight Viagra samples from tier-3 websites. After controlling for testability and authenticity, tier-2 websites are 49.2% cheaper ( p <0.01) and tier-3 websites are 54.8% cheaper ( p < 0.01) than tier-1 sites. These differences are driven by non-Viagra drugs. For Viagra, failing samples are cheaper, but there is no significant price difference across tiers once we condition on testability and authenticity. To study the demand side, we designed a survey that was distributed by RxRights. Among the 2,522 respondents who have purchased prescription medication and are concerned about the price of US pharmaceuticals, results show that 61.54% purchase drugs online and mostly from foreign websites, citing cost saving as the leading reason. Conditional on shopping online, 41.11% check with a credentialing agency. Both samples convey a consistent message that certification agencies deliver useful information for foreign websites and online consumers. Further, while these findings confirm the Food and Drug Administration warning against rogue websites, they do suggest that a blanket ban against all foreign websites may deny consumers substantial savings from certified tier-2 websites.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".