Now for the Good News: Self-Perceived Positive Effects of the First Pandemic Wave on Romantic Relationships Outweigh the Negative
Bibliographic record
Abstract
Media attention has highlighted the COVID-19 pandemic’s negative effects on romantic relationships (e.g., increased partner aggression). The current mixed-method study also explored potential positive effects, and how the relative balance of positive versus negative effects might have changed over time during the first pandemic wave. Individuals ( N = 186) who participated in a pre-COVID study were recruited through MTurk to participate in a four-wave longitudinal follow-up, every 2 weeks from mid-April to late May 2020. Participants completed an 8-item self-report measure assessing perceived negative and positive effects of the pandemic on their romantic relationship. Multi-level models revealed that perceived positive effects were substantially higher than perceived negative effects at each timepoint, even amongst those who reported being more heavily impacted by the pandemic. Both positive and negative effects were stable across time. Open-ended questions at the final time point were coded for common themes. Positive themes were more frequent than negative themes. The most common negative theme centered on increased stress or tension in the relationship, while the most common positive theme discussed the importance of focusing on and appreciating the relationship, including taking advantage of the gift of increased time together the pandemic had brought. Amongst all of the pandemic’s bad news, it is refreshing to consider the possibility of pandemic-related benefits for people’s romantic relationships.
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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 | 0.007 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".