The Effects of Reducing Social Media Use on Body Esteem Among Transitional-Aged Youth
Bibliographic record
Abstract
Introduction. Social media use (SMU) and body image concerns are highly prevalent in youth. Although several studies have shown that high SMU is crosssectionally associated with lower body esteem, experimental evidence is lacking. This pilot study experimentally evaluated the effects of reducing SMU on body esteem among transitional aged youth (TAY) with emotional distress. Methods. Thirty-eight undergraduate students presenting with elevated symptoms of anxiety/depression were randomly assigned to the intervention (n = 16), where SMU was restricted to 60 minutes/day, or to the control group (n = 22), where SMU was not restricted. SMU was monitored via screen-time trackers in participants’ smartphone submitted daily during baseline (1-week) and intervention (3-week) periods. Baseline and post-intervention measurements were taken to assess appearance and weight esteem as well as symptoms of anxiety and depression as secondary outcomes. Results. A significant group × time interaction emerged indicating that the intervention participants showed a significantly greater increase in appearance esteem over the 4 weeks compared to controls. There was no significant between-group difference on change in weight esteem. A significant group × time interaction emerge on anxiety indicating that intervention participants showed a significantly greater improvement in anxiety over the study period compared to controls. There was no significant between-group difference on change in depressive symptoms. Discussion. Reducing SMU may be a feasible and effective method of improving appearance esteem and reducing anxiety in a high-risk population of TAY with emotional distress; however, more high-quality randomized controlled trials are needed to confirm findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".