Limiting Social Media Screen-time: Does Voluntary Reduction Impact Mental Health?
Bibliographic record
Abstract
In recent decades, social media have become immensely popular, with the majority of young adults regularly using at least one platform.Some have claimed that heavy use of social media may adversely affect the wellbeing of young adults, but the evidence to date is mixed and weak.This has not stopped some researchers from arguing that heavy use of social media plays a meaningful, causal role in depression.To assess the validity of these claims, I conducted an experiment replicating and expanding upon the work of Hunt et al. (2018) to assess whether young adults (n = 39; 66.7% female; 66.7% MAge = 19.54) who reported symptoms of depression and/or anxiety would experience decreases in these symptoms, the fear of missing out, and increases in wellbeing after voluntarily reducing their social media screen-time for three weeks.Results indicated that, in comparison to a control group, participants in the experimental group reported marginally significant decreases in both anxiety (p = .056,η 2 = .095)and the fear of missing out (p = .054,η 2 = .097)but no significant changes in depressive symptomology or well-being.Recognizing that the sample was small, the findings suggest that cutting down on heavy use of social media may improve the mental health for symptomatic individuals.Should these results be confirmed in a larger sample, they would provide compelling evidence that heavy use of social media has negative implications for the mental health of individuals with anxiety issues.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".