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Record W2052142204 · doi:10.2196/jmir.1127

Use of an Internet “Viral” Marketing Software Platform in Health Promotion

2008· article· en· W2052142204 on OpenAlexafffundabout
Pierre Gosselin, Philippe Poitras

Bibliographic record

VenueJournal of Medical Internet Research · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsImpactCentre hospitalier universitaire de QuébecInstitut National de Santé Publique du Québec
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsHealth promotionThe InternetContext (archaeology)BusinessPublic healthAgency (philosophy)Public relationsPromotion (chess)Health educationAdvertisingWorld Wide WebMarketingInternet privacyMedicinePolitical scienceComputer scienceNursingSociologyGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.327
GPT teacher head0.566
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2008
Admission routes3
Has abstractyes

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