Social Media Use, Influencer Status, and Outdoor Risk-Taking in Australian Adults: Cross-Sectional Survey
Bibliographic record
Abstract
Background: There is growing awareness of the broader health-related harms of social media; yet, research on social media-related injury mortality and morbidity remains limited. Emerging evidence suggests links between excessive social media use and increased risks of self-harm, cyberbullying-related distress, and dangerous viral challenges, but there has been limited research on the link between time spent on social media and environmental risk-taking, such as risky selfies. However, comprehensive epidemiological studies and policy-driven interventions remain scarce, highlighting the need for further investigation into the public health implications of digital engagement. Objective: This research aimed to examine the relationship among self-reported time spent on social media, influencer status, and risk-taking behaviors among Australians, considering implications for injury prevention. Methods: A cross-sectional survey of Australian social media users (N=509) was conducted using stratified quotas to approximate national distributions by age, sex, and geographical location. Participants reported their average daily time spent on social media, whether they identified as a social media influencer, and whether they had ever engaged in risk-taking behavior to create social media content. Associations between categorical variables (eg, influencer status and risk-taking) were examined using Pearson chi-square tests and supplemented with odds ratios (ORs) and 95% CIs. Independent samples 2-tailed t tests were used to compare mean time spent on social media between risk-takers and non-risk-takers. Results: Among participants, 48 (9.4%) self-reported engaging in risk-taking behavior in the outdoors. Influencers were significantly more likely to report risk-taking (28/58, 48.3%) compared to noninfluencers (20/451, 4.4%; χ²1=110.57, P<.001). Risk-takers (n=48) also spent significantly more time on social media (mean=2.05, SD 1.04) compared to non-risk-takers (n=461; mean 1.37, SD 1.04; t57.22=4.31, P<.001). In multivariate analyses, influencers (OR 20.11), males (OR 2.00), and younger age groups (eg, OR 33.06 for 18-24 vs 55-64 years) had significantly higher odds of reporting risk-taking. Conclusions: Outdoor risk-taking for content creation is associated with influencer status and greater time spent on social media. These findings suggest that policy makers should prioritize regulations addressing risky social media behaviors and hold platforms accountable for promoting harmful content. Social media platforms should implement real-time alerts, pop-up warnings, and geolocated safety information to discourage risky behaviors. Public health practitioners should engage influencers to promote safer content norms and develop targeted injury prevention strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".