Use of an Internet “Viral” Marketing Software Platform in Health Promotion
Bibliographic record
Abstract
BACKGROUND: Health-related websites have become a common tool for public health authorities to inform the general public of their health promotion information and programs. However, building traffic in the cluttered health Internet universe is becoming increasingly complex, costly, and challenging for governmental health promotion websites. In 2006, the Canadian Health Network (CHN), a cooperative program made up of the Public Health Agency of Canada (PHAC) and some 20 health non-governmental organizations (NGOs), was looking for an affordable marketing tool for the promotion of its website and contents to specific populations. OBJECTIVE: To test new and innovative marketing tools for a health promotion website in Canada. METHODS: Within the context and constraints of a governmental health promotion website, an adaptation of a commercial Internet viral marketing software platform was developed and implemented under the name "The Crazy Race". This process was done interactively between seven NGOs and the CHN staff. The communication objectives were (a) to provide a meaningful visit that could communicate important public health messages, and (b) to increase subscriptions to its e-newsletter. A nine-step standardized Web-user experience (Internet path) was thus defined and experimented with under a pre-determined operating budget of less then Can$50,000, mainly paid for by participating organizations on a pay-per-performance basis. RESULTS: An initial group of 215 people were sent an invitation to participate in the campaign. Over its 15-day duration, the campaign generated by itself and without any media support a total of 110,200 Web user participants who registered and sent a total of 439,275 invitations (2% of the Canadian Web-user population of 21.8 million in 2006). The epidemic self-dissemination of the campaign occurred in both French and English populations and spread across all age groups. Two-thirds of the participants were women. CONCLUSIONS: The use of an Internet viral marketing platform proved to be effective in bringing thousands of Web users to discover and explore a governmental health promotion website. The exponential growth of the person-to-person dissemination generated by the campaign indicates that public health messages have high viral propagation potential on the Internet ("virulence") when they are presented in the context of an enjoyable online game. This could constitute a promising method to create affordable mass audience public health campaigns, both in Canada and internationally.
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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 | 0.055 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".