Social Media Posts from Friends during Late Adolescence as Predictors of Young Adult Physical Health
Bibliographic record
Abstract
Although an increasing body of literature has linked social experiences to physical health, research has yet to consider how specific aspects of social experiences taking place on social media during late adolescence may predict future physical health outcomes. This study thus examined qualities of social media posts received from peers at age 21 as predictors of participants' physical health (e.g., Interleukin-6 (inflammation), sleep problems, problems with physical functioning, and BMI) at age 28. Participants included 138 youth (59 men and 79 women); 57% of participants identified as White, 30% as Black/African American, and 13% as from other or mixed racial/ethnic groups. Posts from friends and participants at age 21 characterized by social ties predicted lower levels of future physical health problems, whereas socially inappropriate "faux pas" posts that deviated from peer norms by friends predicted higher levels of physical health problems at age 28. These associations were found after accounting for factors typically associated with physical health outcomes, including participants' baseline social competence, internalizing and externalizing symptoms, alcohol use, observed physical attractiveness, and history of prior hospitalizations. The results of this study suggest the importance of both achieving social integration with peers online and adhering to peer norms in the online domain as key predictors of future physical health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".