Social Media and Selfie-Related Mortality Amid COVID-19: Interrupted Time Series Analysis
Bibliographic record
Abstract
BACKGROUND: COVID-19 had a considerable impact on mortality, but its effect on behaviors associated with social media remains unclear. As travel decreased due to lockdowns during the pandemic, selfie-related mortality may have decreased, as fewer individuals were taking smartphone photographs in risky locations. OBJECTIVE: In this study, we examined the effect of the COVID-19 pandemic on trends in selfie-related mortality. METHODS: We identified fatal selfie-related injuries reported in web-based news reports worldwide between March 2014 and April 2021, including the deaths of individuals attempting a selfie photograph or anyone else present during the incident. The main outcome measure was the total number of selfie-related deaths per month. We used interrupted time series regression to estimate the monthly change in the number of selfie-related deaths over time, comparing the period before the pandemic (March 2014 to February 2020) with the period during the pandemic (March 2020 to April 2021). RESULTS: The study included a total of 332 selfie-related deaths occurring between March 2014 and April 2021, with 18 (5.4%) deaths during the pandemic. Most selfie-related deaths occurred in India (n=153, 46.1%) and involved men (n=221, 66.6%) and young individuals (n=296, 89.2%). During the pandemic, two-thirds of selfie-related deaths were due to falls, whereas a greater proportion of selfie-related deaths before the pandemic were due to drowning. Based on interrupted time series regression, there was an average of 1.3 selfie-related deaths per month during the pandemic, compared with 4.3 deaths per month before the pandemic. The number of selfie-related deaths decreased by 2.6 in the first month of the pandemic alone and continued to decrease thereafter. CONCLUSIONS: Our findings indicate that the COVID-19 pandemic led to a marked decrease in selfie-related mortality, suggesting that lockdowns and travel restrictions likely prevented hazardous selfie-taking. The decrease in selfie-related mortality occurred despite a potential increase in social media use during the pandemic.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".