Internet health scams—Developing a taxonomy and risk‐of‐deception assessment tool
Bibliographic record
Abstract
The prevalence of health scams in Canada is increasing, facilitated by the rise of the Internet. However, little is known about the nature of this phenomena. This study sought to methodically identify and categorise Internet-based Health Scams (IHS) currently active in Canada, creating an initial taxonomy based on systematic Internet searches. A five-step Delphi approach, comprised of a multidisciplinary panel of health professionals from the University of British Columbia, in Vancouver, Canada, was used to establish consensus. The resulting taxonomy is the first to characterise the nature of IHS in North America. Five core areas of activity were identified: body image products, medical products, alternative health services, healthy lifestyle products, and diagnostic testing services. IHS purveyors relied on social expectations and psychological persuasion techniques to target consumers. Persuasion techniques included social engagement, claims of miraculous effects, scarcity, and the use of pseudoscientific language. These techniques exploited personality traits of sensation seeking, needing self-control, openness to taking risks, and the preference for uniqueness. The data gathered from the taxonomy allowed the Delphi panel to develop and pilot a simple risk-of-deception tool. This tool is intended to help healthcare professionals educate the public about IHS. It is suggested that, where relevant, healthcare professionals include a general discussion of IHS risks and marketing techniques with clients as a part of health promotion activities.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.025 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".