Social Media as a Predictor of Depression Rates Among Male Versus Female Adolescents During the COVID-19 Pandemic
Bibliographic record
Abstract
Technology use has drastically and progressively increased as the COVID-19 pandemic has continued to unfold. Adolescents are now reliant on technology for their education, in addition to communication with friends and family (Pfefferbaum & North, 2020). With the recency of the pandemic, research on the effects of increased internet and social media use for adolescent mental health is decidedly underdeveloped. This study aimed to fill the research gap by examining how the frequency of male and female adolescents’ social media use is associated with depression rates during the pandemic by using a longitudinal design. Participants for this study included 351 adolescents, ages 14-19, residing in Ontario, Canada. Participants completed two surveys: the first (Time 1) was conducted between April 4th to April 16th, 2020, approximately three weeks following secondary school closures in Ontario, Canada due to the COVID-19 pandemic. The second survey (Time 2) was conducted between August 21st and September 6th, approximately six months following the first lockdown orders. The findings indicate that, in line with hypotheses, females engaged in more social media use and experienced greater depression than males. Regression analyses further revealed that Time 1 social media use was a significant predictor of Time 2 depression in females only. Strengths, weaknesses, implications, intervention strategies, and future directions for research addressing social media and depression are also 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".