Public perceptions of Internet‐based health scams, and factors that promote engagement with them
Bibliographic record
Abstract
The prevalence of health scams in Canada is increasing, facilitated by the rise of the Internet as a mass communication medium. However, little is known about the nature of this phenomena. Building on previous work exploring the nature of Internet health scams (IHS), this project sought to better understand the reasons why people engaged with IHS, and if contemporary psychosocial theory can help explain IHS engagement. A mixed-methods study, involving a web-based survey incorporating qualitative questions and the Susceptibility to Persuasion-II Brief psychometric scale (STP-II Brief), were administered (N = 194) in British Columbia, Canada, in 2017. Results (n = 156) demonstrated that 40% of participants had ever engaged with IHS, but only 1% reported to have actually lost money to a deceptive product/service. Associations between scam engagement, participant demographics and STP-II Brief scores were explored, with Sex and Employment Status both found to have a significant effect on odds of IHS engagement. STP-II Brief scores were positively correlated with a likelihood of engagement with IHS, even when adjusting for demographic characteristics. The types of IHS most frequently engaged with were those related to body image products, and social influence appeared to be a dominant psychosocial factor promoting engagement. Participants reported that claims of products being 'natural', the result of scientific breakthroughs, use of pseudoscientific language, use of testimonials, and celebrity or professional endorsement could lead them to engage with a product. These findings can help inform health professionals' understanding of public health-seeking behaviours with respect to deceptive marketing.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".