Curvilinear associations between neuroticism and dyadic adjustment in treatment-seeking couples.
Bibliographic record
Abstract
Among personality traits, neuroticism has been shown to be the most significant predictor of dyadic adjustment. Despite some propositions arguing that low, as well as high levels of personality traits are maladaptive tendencies, only the negative linear relationship between neuroticism and couple satisfaction has been addressed in past research. The aim of this study was to examine the nonlinear association between neuroticism and dyadic adjustment for both partners of a clinically distressed sample of couples. The sample included 472 couples seeking couple therapy who completed the NEO-FFI (P. T. Costa & R. R. McCrae, 1992, NEO PI-R professional manual, Odessa, FL, Psychological Assessment Resources) and the Dyadic Adjustment Scale (G. B. Spanier, 1976, Measuring dyadic adjustment: New scales for assessing the quality of marriage and similar dyads, Journal of Marriage and the Family, 38, pp. 15-28). Results showed, for actor and partner effects, a significant nonlinear, inverted U-shaped relationship between neuroticism and dyadic adjustment. In particular, both very low levels and high levels of neuroticism were associated with lower dyadic adjustment for both the individual and his or her partner. This finding is in contrast with the traditional negative linear association between neuroticism and dyadic adjustment observed in previous research. Openness and agreeableness also positively predicted self and partner dyadic adjustment. Findings bear important clinical implications for therapists assessing and working with distressed 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".