Smartphones Undermine Social Connectedness More in Men Than Women: A Mini Mega-Analysis
Bibliographic record
Abstract
Though smartphones have been shown to undermine well-being and social connection, evidence also suggests that these effects depend on when and how people use their phones. To examine whether the effects of phones on well-being (affect valence) and social connection depend on the situation, we compiled data across eight published and unpublished experiments where phone use was manipulated (N = 1,778). These experiments included situations ranging from parents visiting a science museum with their children, eating a meal with a group of strangers, to looking for an unfamiliar building. We found that phones have a significant negative impact on people’s feelings of social connectedness across situations. The impact of phones on well-being, however, depended on the situation: Phones negatively impacted well-being when used during ongoing social interactions, but not when used to find information relevant to current goals. Our large dataset also allowed us to examine whether the effects of phones depend on individual differences. We found that gender moderated the effects of phones on social connectedness, whereby phones negatively impacted men more than women. Overall, even after including unpublished studies with nonsignificant findings, we find that the negative effects of phones on well-being and social connection persist. Going beyond past research, however, we also show that these negative effects on social connection are driven by men more so than women.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".