Logging out or leaning in? Social media strategies for enhancing well-being.
Bibliographic record
Abstract
Social media use is endemic among emerging adults, raising concerns that this trend may harm users. We tested whether reducing the quantity of social media use, relative to improving the way users engage with social media, benefits psychological well-being. Participants were 393 social media users (ages 17-29) in Canada, with elevated psychopathology symptoms, who perceived social media to negatively impact their life somewhat. They were randomized to either (a) assistance to engage with social media in a way to enhance connectedness (tutorial), (b) encouragement to abstain from social media (abstinence), or (c) no instructions to change behavior (control). Participants' social media behaviors were self-reported and tracked using phone screen time apps while well-being was self-reported, over four timepoints (6 weeks in total). Results suggested that the tutorial and abstinence groups, relative to control, reduced their quantity of social media use and the amount of social comparisons they made on social media, with abstinence being the most effective. Tutorial was the only condition to reduce participants' fear of missing out and loneliness, and abstinence was the only condition to reduce internalizing symptoms, relative to control. No condition differences emerged in eating pathology or the tendency to make social comparisons in an upward direction. Changes in social media behaviors mediated the effects of abstinence (but not of tutorial) on well-being outcomes. Participant engagement and perceptions of helpfulness were acceptable, but the abstinence group possibly perceived the content as less helpful. In conclusion, using social media differently and abstaining from social media may each benefit well-being. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".