There is More to Snapchat than Snapping: Examining Active and Passive Snapchat Use as Predictors of Anxiety in Adolescents
Bibliographic record
Abstract
Social media’s adoption in society continues to increase, and past research has found significant relationships between social media use and anxiety in young adolescents. The current research focused solely on Snapchat, as it is currently one of the most popular platforms among adolescents; however, it is also one of the least researched. This research aimed to explore Active and Passive Snapchat Use as predictors of anxiety in adolescents over time. This study focused on what people are actually doing while using Snapchat rather than the amount of use. Adolescents (N = 105, 21.2% male and 78.8% female) from High Schools in Ontario, Canada in grade 9 to grade 12 completed an online survey, responding to questions about their anxiety levels and social media use, which included the developed Active and Passive Use measures. The same sample (N = 46, 15.6% male and 84.4% female) completed the survey again, 3 months later. It was hypothesized that participants who demonstrated higher frequencies of Active Snapchat Use at Time 1, would have decreased levels of anxiety at Time 2 (H1). It was also hypothesized that those who demonstrated higher frequencies of Passive Snapchat Use at Time 1, would have increased anxiety levels at Time 2 (H2). H1 and H2 were not supported, however, results indicated that higher frequencies of Active Snapchat Use at Time 1 predicted increased anxiety at Time 2. Results indicate the significance of different Snapchat-related behaviours on anxiety levels in adolescence. Study limitations and directions for future research are discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".