Web 2.0 and its implications for health-related social marketing campaigns
Bibliographic record
Abstract
Web 2.0, or new media, is increasing in usage and adoption over time. Research demonstrates that new media channels are being used by demographic groups, varying proportions of which seek health information on these platforms. Health promotion and social marketing practitioners as well as researchers are beginning to use Web 2.0 to market health-related behaviour change.\nAs the prevalence of Web 2.0 usage increases, and its nature changes, over time there are areas which warrant research examination, such as: reasons for Web 2.0 growth and adoption, the characteristics which draw different target audiences to Web 2.0 platforms, as well as the best practices that researchers and practitioners should consider when using new media channels to market health-related behaviour change.\nThis thesis contributes to our understanding of how health promotion and social marketing practitioners, as well as researchers, can use Web 2.0 to market and research health-related behaviour change.\nThis thesis contains seven articles and uses a mixed methodological approach, including qualitative and quantitative methods, in three stages. Stage 1 involved a systematic literature review and online search to document Web 2.0 reach and growth over time, user engagement in Web 2.0, and theoretical frameworks to explain why users adopt and diffuse Web 2.0. The results of this review are presented in Chapters 2 and 3; and led to determining the gaps in the Web 2.0 evidence base, which was the basis of the papers presented in Chapters 6 and 7.\nSignificant increases over time in reach and growth of Web 2.0 and new media channels by geography, technology, and age were found during the systematic literature review and online search. Users engaged in Web 2.0 channels to seek or create news, entertainment, and health information. An examination of the practical use of social marketing principles in Web 2.0 campaigns suggested that one or more principles were being applied to new media campaigns, but further coordination and integration are required.\nA framework to guide the development of a Web 2.0 research agenda identified many opportunities for Web 2.0 researchers to advance the field: documenting Web 2.0 natural history, individual risks or benefits, and specific areas relevant to the discipline of social marketing (acceptance, utilisation, social marketing principles, or best practices).\nStage 2 involved qualitative in-depth interviews (presented in Chapters 4 and 5) with 17 social marketing experts living in Australia, Canada, Europe, New Zealand, the United Kingdom, and the United States who had utilised Web 2.0 marketing strategies. The discussion guide contained open-ended questions which gauged: reasons for campaign implementation, formative and pre-test research strategies used, campaign implementation strategies used, how campaign impact was defined, and how campaign success was measured.\nDuring the qualitative in-depth interviews, Web 2.0 experts working in social marketing discussed best practices from utilising new media channels; such as using plain language, transparent communication practices, and strategic planning for social marketing and health promotion campaigns. Interestingly, practitioners perceived more strengths than limitations with implementing a campaign on Web 2.0 platforms compared to implementing a campaign on traditional communication channels. Social marketing practitioners also used some traditional research approaches to evaluate their Web 2.0 campaigns.\nThe last phase of the research comprised a quantitative online survey (n=325) with Internet users 18 years of age and older living in Australia, Canada, and the United Kingdom (Chapter 8) to assess usage of Web 2.0 for health information and health engagement. New media platforms were visited by Internet users living in Australia, Canada, the United Kingdom, and the United States for health information and to engage in health content engagement. To reach individuals 18-34 years old, recommendations were provided to use video sharing or social media channels (Facebook or Twitter), whereas, for Internet users 55 years of age and older Twitter and LinkedIn were equally good avenues for disseminating health information, while Facebook was not.\nThe overall results of the enclosed thesis provide concrete recommendations for practitioners and researchers with regard to how to plan, monitor, and evaluate best-in-class health promotion and social marketing campaigns using Web 2.0. Modelling healthy behaviour, engaging target audiences in creating content, and disseminating messages on multiple Web 2.0 channels were examples of best practices for Web 2.0 campaigns.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".