The affective dynamics of reassurance-seeking in real-time interactions
Bibliographic record
Abstract
According to several interpersonal theories of depression, excessive reassurance-seeking is one way depressed individuals may contribute to relationship deterioration, particularly in romantic relationships. The present study is the first behavioral investigation of the hypothesis that reassurance-seeking induces negative affect in interaction partners in real time. This study also investigates potential affective precursors to reassurance-seeking behaviors. A videotaped discussion task completed by 121 women and their male romantic partners was behaviorally coded to assess reassurance-seeking and affect in both members of the couple. Female depression was measured via self-report. Results indicated that the association between female reassurance-seeking and male partner’s subsequent anxiety approached statistical significance, but no other significant relations between reassurance-seeking and partner affect were found. Additionally, female anxious affect positively predicted subsequent reassurance-seeking, and depression symptoms moderated this relation. Unexpectedly, the relation between anxious affect and reassurance-seeking was stronger among women with less severe depression symptoms. Our findings support integrated and revised theories of reassurance-seeking and underscore the need to further investigate reassurance-seeking behavior in real-time interactions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".