Privacy Policies: A Study of Their Use Among Online Canadian Pharmacies
Bibliographic record
Abstract
The use of online Canadian pharmacies has grown over the past decade due to lower cost medications and ease of use. In order for these firms to gain business and marketing information, they collect a variety of consumer data. This has raised concerns among consumers as to privacy issues of the data collected by these online firms. However, researchers have not effectively examined how online consumers value specific privacy factors when deciding whether to use the sites. Also, studies have not determined if many of these sites have comprehensive privacy policies that indicate if they protect consumers' data for a variety of factors. This research included a study of 25 major online Canadian pharmacies to determine the completeness of privacy policy factors among this population. This survey showed the majority of sites did contain a privacy policy. However, the comprehensiveness of policies differed vastly among the sites. This dissertation also included an investigation of consumers' views of the privacy policy factors they feel are important when deciding to use these pharmacy sites. Results of a survey of 147 users of medical Web sites showed that consumers were concerned about privacy on these sites, with opt-in, security and consumer/licensing issues of high importance. However, the study also showed that for consumers who actually used an online pharmacy during the past year, cost savings, rather than privacy issues were the principal concern. This dissertation created an instrument that online firms can use to evaluate consumers perceptions of privacy policies, as well as which policies are important to include on a Web site.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".