Couple attachment and the quality of marital relationships: Method and concept in the validation of the new couple attachment interview and coding system
Bibliographic record
Abstract
This study investigates links between adult attachment and marital quality in 73 married couples, using a new Couple Attachment Interview that was modeled after the Adult Attachment Interview but focuses on the relationship between the partners. A coding system (CAICS) comparing the interview protocol to prototypes for secure, dismissing, and preoccupied attachment styles yielded continuous ratings of all three styles, and categorical classifications of secure/insecure for each partner. The study found direct links between couple attachment and both self-reported and observed marital quality, with all three continuous scores contributing uniquely to the equations. In most cases, the continuous scores explained variation in marital quality after the categorical security scores were entered into the regressions, although categorical scores also contributed uniquely to the explanation of marital quality. Pairing of partners' scores explained significant variance in both self-reported and observed evaluations of the couple relationship. Security of couple attachment served as a mediator in the link between self-reported marital satisfaction and observed marital quality. The results illustrated the interconnection of methodological choices and theoretical advances in the study of attachment and couple relationship quality.
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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 | 0.059 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".