Bibliographic record
Abstract
This study examined whether an individual's attachment style and/or a couple's combination of attachment styles predicted violence within the marriage and explored whether other variables moderated the risk of violence. Measures of attachment style were administered to 41 discordant couples who presented to four different clinics. The couples' presenting complaints were not violence, and those who did report violence on questioning did not manifest severe violence, i.e., requiring shelters or legal intervention. Self-report measures of violence and marital satisfaction, including problem-solving communication, were also given. Using analysis of covariance and logistic regression, the relative contributions to strength of predicting being a victim of conjugal violence were calculated. An anxious attachment style was a significant predictor of females being victims of violence and of men not being victims. A dismissive style in men was predictive of men being victims when entered into the model with problem solving communication. The combination of anxiously attached females and dismissive males was a potent predictor of violence, and longer duration of marriage and poor problem-solving communication added power to the prediction. Marital interaction, which is influenced by couples' attachment styles and problem-solving communication, is a significant factor in marital partners experiencing physical violence. For couples with milder levels of violence, a more nuanced approach (compared with the legally based approach used for severe violence) seems indicated.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".