Changes in marital relationships over the course of the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
ABSTRACT Objective The aim of this study was to document changes in marital relationships over the course of the COVID‐19 pandemic. Background Research has suggested both positive and negative effects of the pandemic on marital relationships, but few studies have explored changes in relationship quality at different phases of the pandemic. Method Online survey data were collected from married individuals ( n = 3,221, mean age 39.5 years, SD = 5.61) living in the United States at three time points during the pandemic: April 2020, December 2020–January 2021, and August–September, 2021. We report the findings on responses to an open‐ended question: “Please explain how your relationship has changed over the course of the coronavirus.” Inductive qualitative content analysis was conducted. Results Most participants reported some changes in their relationships; positive changes were more prevalent than negative changes across the three time periods. Discussion Our findings are consistent with previous literature but also provide new insights into how marriages may have been differentially affected at early versus later stages of the pandemic. Implications Clinical implications of the findings are discussed, including specific recommendations for therapists working with couples.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".