The relationship between social media use and loneliness across the lifespan in the United States: Population-based study using Health Information National Trends Survey data
Bibliographic record
Abstract
Objectives: Loneliness can affect all age groups and is continuing to grow as a major public health issue. One approach that has been proposed to address loneliness involves digital inclusivity because the ubiquity of technology and social media provides new avenues for activities to satisfy individuals' needs for social connection. It is unclear which age groups could benefit more from using social media related to loneliness. The current study advances the limited research on the relationship between social media use and loneliness looking across multiple age groups with nationally representative data. Methods: Data were analyzed from the 2022 Health Information National Trends Survey (HINTS; n = 4774). This study used multiple linear regression stratified by four age cohorts (Millennials, Generation X, Baby Boomers, Silent Generation) to examine the association of social media use with PROMIS Social Isolation scores and to assess differences across age cohorts. Covariates included age, gender, race and ethnicity, education, income, living with other adults, marital status, having a friend to talk to about health, self-rated health, PROMIS Meaning and Purpose, and the PHQ-4. Results: = .04) was associated with a higher loneliness score. There was not a significant relationship between social media and loneliness for Millennials and Generation X. Conclusions: Social media use may be a risk factor for loneliness particularly for those who are from the Baby Boomer and Silent Generation and use social media daily. Social media interventions aimed at decreasing loneliness among older adults should consider the directional of this relationship, which needs to be confirmed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".