A Survey of Health-Related Activities on Second Life
Bibliographic record
Abstract
BACKGROUND: Increasingly, governments, health care agencies, companies, and private groups have chosen Second Life as part of their Web 2.0 communication strategies. Second Life offers unique design features for disseminating health information, training health professionals, and enabling patient education for both academic and commercial health behavior research. OBJECTIVES: This study aimed to survey and categorize the range of health-related activities on Second Life; to examine the design attributes of the most innovative and popular sites; and to assess the potential utility of Second Life for the dissemination of health information and for health behavior change. METHODS: We used three separate search strategies to identify health-related sites on Second Life. The first used the application's search engine, entering both generic and select illness-specific keywords, to seek out sites. The second identified sites through a comprehensive review of print, blog, and media sources discussing health activities on Second Life. We then visited each site and used a snowball method to identify other health sites until we reached saturation (no new health sites were identified). The content, user experience, and chief purpose of each site were tabulated as well as basic site information, including user traffic data and site size. RESULTS: We found a wide range of health-related activities on Second Life, and a diverse group of users, including organizations, groups, and individuals. For many users, Second Life activities are a part of their Web 2.0 communication strategy. The most common type of health-related site in our sample (n = 68) were those whose principle aim was patient education or to increase awareness about health issues. The second most common type of site were support sites, followed by training sites, and marketing sites. Finally, a few sites were purpose-built to conduct research in SL or to recruit participants for real-life research. CONCLUSIONS: Studies show that behaviors from virtual worlds can translate to the real world. Our survey suggests that users are engaged in a range of health-related activities in Second Life which are potentially impacting real-life behaviors. Further research evaluating the impact of health-related activities on Second Life is warranted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".