Love under lockdown: How changes in time with partner impacted stress and relationship outcomes during the COVID-19 pandemic
Bibliographic record
Abstract
With the onset of COVID-19, governments around much of the world implemented strict social distancing and stay-at-home orders that profoundly affected the amount of time many couples were spending together. In the present research, we examined whether perceptions of a change in time spent with a partner were associated with stress, and whether stress levels in turn predicted relationship commitment and satisfaction, both in the short term (Time 1) and longer term (Time 2; i.e., after 10 months). Results indicated partial mediation, such that less (vs. more) time spent with the partner was associated with greater stress at Time 1, which in turn partly accounted for lower commitment and relationship satisfaction both at Time 1, and satisfaction at Time 2. Less (vs. more) time spent with partner at Time 1 also predicted a greater likelihood of relationship dissolution at Time 2, again partially mediated by stress. An increase in quality time spent together at Time 2 predicted stress and relationship outcomes over and above the change in time spent together more generally. This research has important implications for understanding the ongoing effects of the pandemic on romantic relationships. In addition, this study provides new evidence regarding how changes in time spent with a partner are associated with stress and subsequent relationship outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".