The social ambivalence and disease model: Childhood trauma as an antecedent factor linking spousal ambivalence to inflammation.
Bibliographic record
Abstract
OBJECTIVE: Ambivalence in relationships is related to health-relevant biological outcomes. However, the antecedent processes that contribute to this association are unknown. The primary aim of this study was to test the prediction of the social ambivalence and disease model, which highlights the potential role of childhood trauma as an antecedent factor linking spousal ambivalence to inflammation. METHOD: A sample of 107 heterosexual couples who had been married for at least 10 years was recruited. Participants completed the social relationship index to assess spousal ambivalence and the Childhood Trauma Questionnaire. Blood was drawn to determine levels of high-sensitivity C-reactive protein (hs-CRP) and interleukin-6 as measures of inflammation. RESULTS: Consistent with the social ambivalence and disease model, there was a significant indirect effect in which childhood trauma was related to greater spousal ambivalence which in turn was associated with higher hs-CRP levels. No evidence for the statistical mediational model was found for interleukin-6. CONCLUSIONS: These results highlight the potential role of childhood trauma as an antecedent factor linking spousal ambivalence to hs-CRP. It also highlights potential pathways that might be targeted for interventions pending further work. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".