Marital distress and depressive symptoms in women: The effects of self-silencing and self-complexity.
Bibliographic record
Abstract
Marital distress and depression are strongly related, making the study of depression within a marital context particularly interesting. Causal models suggest that men's depressive symptoms precede marital dissatisfaction, whereas women's depressive symptomatology follows marital dissatisfaction. Few such models have integrated husbands' and wives' variables in a single model. The present study tested a model that predicted depressive symptoms in married women using marital dissatisfaction, self-silencing, and husbands' depressive symptoms. Jack's (1991) theory predicted that self-silencing would be more likely to occur in women for whom the marital role was central to the self-concept. A community sample of eighty-five couples completed the Beck Depression Inventory, and the Revised Dyadic Adjustment Scale. A measure self-image complexity was included to determine the extent to which subjects defined themselves in terms of their marital relationships. A "domino effect" was supported in predicting women's, but not men's, depressive symptoms: depressed husbands tended to be dissatisfied with their marriages, which increased the likelihood that their wives would also be dissatisfied, which was related to the women's vulnerability to depressive symptoms. Silencing one's needs and feelings within relationships was also associated with an increase in women's depressive symptoms, and was particularly likely to occur when the husbands reported depressive symptoms. Contrary to Jack's (1991) self-silencing theory, silencing was less likely to occur in women who defined themselves in terms of their marital relationships. This finding is in agreement with research examining relationships between conflict management techniques and particular attachment styles. Individuals who are preoccupied with their relationships, who are likely to define themselves in terms of those relationships, tend not to silence their needs and feelings. In contrast, individuals who avoid closeness in relationships, who are unlikely to define themselves in relationship terms, tend to withdraw from conflict and censor their feelings in interactions with their partners. Further research is needed in order to clarify the association between the centrality of relationships to one's sense of self and the silencing of needs and feelings within those relationships.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".