Bibliographic record
Abstract
The advent of Internet technology has affected the pharmaceutical industry in at least two ways. First, existing companies have implemented Internet solutions for efficiency and performance reasons. These solutions may convey benefits across the entire manufacturer—wholesaler—pharmacy supply chain (e.g., inventory tracking and management), or focus at one level (e.g., providing customer information from a pharmacy Web site). Second, new businesses have been established to capitalize on the opportunities made possible by Internet technology. The new businesses may be related to others, as in the case where storefront pharmacies have established online companies to expand their market scope. Two other new business types represent more radical change and are controversial. The first uses the Internet to deliver information about specific drugs through spam (unsolicited commercial or bulk e-mail). Relatively little is known about such pharmacies, although they account for a large and growing proportion of all spam (PRWeb, 2004). They often promote dubious products and cures, may not require a prescription, and actual delivery is not assured (Barrett, 2001). Some customers buy from such pharmacies in spite of these problems. Because there is a dearth of research on pharmacies using spam, and given that their ethics and standards are at best highly questionable, they are not considered here. A second type of pharmacy has gained prominence since 1999. The Internet pharmacies in question are start-ups that operate wholly online and have no connection to existing pharmacies. For the most part, these Internet pharmacies export prescription drugs from a lower cost country to one or more where higher costs prevail. In North America, many Internet pharmacies have sprung up in western Canada to supply drugs to U.S. consumers. International trade in prescription drugs is also seen elsewhere, again motivated by different price levels.1 The North America experience is discussed next. Canadian Internet pharmacies have achieved success since 1999, but face an uncertain future. Their emergence is traced below and the major points of controversy identified. The analysis reveals that the application of Internet technology in these pharmacies was quite straightforward. More problematic is the complex, political environment in which Internet pharmacies operate, and the fact that the strategy adopted challenges established legal and ethical standards. These issues are addressed in the final sections where the future of Internet pharmacies is examined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".