PARTNER CORRESPONDENCE IN SOCIAL INTERACTION TIME AND WELL-BEING: EVERYDAY EVIDENCE FROM OLDER DYADS
Bibliographic record
Abstract
Abstract Social interactions play an important role in older adults’ daily well-being. Longer duration of interaction has been associated with higher well-being at the individual level. However, less is known about the extent to which partners differ in their convergence in interaction reports and whether such differences might be associated with daily well-being. Positive Illusion theory states that individuals are more content in relationships if they idealize their partner leading to the assumption that overreporting interaction time may benefit well-being. Based on the literature, we expect that longer social interaction time is associated with higher well-being at the individual level. At the partner level, we look at discrepancies and similarities. We assess whether individuals report higher well-being on days when they overreport the interaction time with their partner. We further test whether partners who are more similar in evaluating the time spent together are also more similar in their well-being reports. This study uses dyadic end-of-day diary data from 136 Canadian older adults (M = 66.68 years, SD = 13.11 range: 18-87 years, 60% women) and a close other of their choice. Preliminary analyses indicate that longer duration of interaction was associated with higher well-being. Correlations show differences in interaction time reports between partners. Multilevel models will examine whether overreporting interaction time may be associated with well-being. Further analyses will investigate how synchrony in interaction time reports shape joint well-being. As data collection is completed, these results will be ready to be presented by the time of GSA conference in November.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".