Is Well-Being Associated with the Quantity and Quality of Social Interactions?
Bibliographic record
Abstract
Social relationships are often touted as critical for well-being. However, the vast majority of studies on social relationships have relied on self-report measures of both social interactions and well-being, which makes it difficult to disentangle true associations from shared method variance. To address this gap, we assessed the quantity and quality of social interactions using both self-report and observer-based measures in everyday life. Participants (N = 256, 3,206 observations) wore the Electronically Activated Recorder (EAR), an unobtrusive audio recorder, and completed experience sampling method (ESM) self-reports of their momentary social interactions, happiness, and feelings of social connectedness, four times each day for one week. Observers rated the quantity and quality of participants’ social interactions based on the EAR recordings from the same time points. Quantity of social interactions was robustly associated with greater well-being in the moment and on average, whether they were measured with self-reports or observer reports. Conversational (conversational depth and self-disclosure) and relational (knowing and liking one’s interaction partners) aspects of social interaction quality were also generally associated with greater well-being, but the effects were larger and more consistent for self-reported (vs. observer-reported) quality variables, within-person (vs. between-person) associations, and for predicting social connectedness (vs. happiness). Finally, although most associations were similar for introverts and extraverts, our exploratory results suggest that introverts may experience greater boosts in social connectedness, relative to extraverts, when engaging in deeper conversations. This study provides compelling multi-method evidence supporting the link between more frequent and deeper social interactions and well-being.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".