Taking stock of the longitudinal study of romantic couple relationships: The last 20 years
Bibliographic record
Abstract
Abstract Longitudinal dyadic research provides significant benefits for our understanding of romantic couple relationships. In this systematic review, we begin by providing a broad overview of topical trends and approaches in longitudinal couple relationships research from 2002 through 2021. Then, we narrow our review to dyadic relationship quality articles, highlighting key themes as well as noting important gaps in the research. Using an intersectional perspective that acknowledges multiple ways that disadvantage, power, and oppression may be seen in both research and in couples' lived experience, we note prominent paradigms used in examining couple relationships, what types of questions have been most valued, and what groups and approaches are underrepresented in the literature. Most longitudinal couple relationships research is quantitative, relies on self‐report approaches from American couples in the early‐to‐middle years of their relationships, concentrates more on negative aspects of relationships than positives, and takes a communication‐satisfaction paradigm in studying couples. We see a clear need to increase the use of methodologies beyond self‐report measures, conduct more studies with within‐group minority, older adult, culturally‐diverse, and context‐specific samples to explore the diversity of relationships, and fully consider both strengths and positive processes in relationships as well as the challenges couples experience.
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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 | 0.040 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".