Use of Web 2.0 to Recruit Australian Gay Men to an Online HIV/AIDS Survey
Bibliographic record
Abstract
BACKGROUND: Continuous prevention efforts for human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) are recommended among those men who have sex with men (MSM). Creative use of e-technologies coupled with a better understanding of social networks could lead to improved health interventions among this risk population. OBJECTIVE: The aims of the study were to (1) compare the impact of various advertising strategies on recruiting MSM participants to an online HIV/AIDS survey, and (2) explore the feasibility of using a social network service (SNS) for study advertising. METHODS: A cross-sectional online survey was conducted in 2009. South Australian men over 18 years were invited to participate if they had had sexual intercourse with men in the previous year. A short questionnaire was used to collect demographics and information on sexual behavior, HIV history, use of the Internet for dating purposes, and sources of health information. The survey was promoted in community settings and online, including advertisements through social networks. RESULTS: A total of 243 men completed the online survey during the 8-week data collection period. Online advertisements recruited 91.7% (220/240) of the sample. Conversely, traditional advertisements in the community recruited only 5.8% (14/240) of the sample. Ten volunteers were asked to advertise on their personal SNS application, but only 2 effectively did so. Only 18/240 (7.5%) of the respondents reported having learned of our study through the SNS application. In this sample, 19.3% (47/243) of participants had never been tested for HIV. Among the participants who had been tested, 12.8% (25/196) reported being HIV-positive. Regarding Internet use, 82.3% (200/243) of participants had dated online in the previous 6 months. Among the participants who had dated online, most (175/200, 87.5%) had found an Internet sexual partner and two-thirds (132/200, 66.0%) had had anal sex with these partner(s). Among men who had anal sex with an Internet partner, 68.2% (90/132) used a condom during sex. CONCLUSIONS: The MSM participants in this study had high-risk profiles for HIV and other sexually transmitted diseases (STDs), which highlights the need for ongoing health interventions among this group. In this study, the SNS marketing strategy did not appear to create a viral effect and it had a relatively poor yield.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".