Benefits of positive relationship experiences for avoidantly attached individuals.
Bibliographic record
Abstract
Attachment avoidance is characterized by discomfort with closeness and a reluctance to develop intimacy with romantic partners, which contribute to heightened general negativity and lower satisfaction and self-disclosure in and out of their relationships. Recent research, however, has begun to uncover circumstances in which romantic partners and positive relationships buffer more avoidantly attached individuals against deleterious individual and relationship outcomes. Across 3 studies, using a multimethod approach encompassing both experimental and dyadic longitudinal diary methods, we investigated the effects of positive, intimacy-related relationship experiences on more avoidant persons' positive and negative affect, relationship quality, self-disclosure, and attachment security immediately and over time. Results revealed that more avoidant individuals exhibit a reduction of general negative affect in particular (Studies 1-2) and report greater relationship quality (Studies 2-3) in response to positive relationship experiences, and, following intimacy-promoting activities with their partner, engage in greater self-disclosure over time and demonstrate decreased attachment avoidance 1 month later (Study 3). These findings identify novel circumstances in which more avoidant persons' negative expectations of relationships may be countered, and suggest that relatively simple techniques can have potentially important short- and long-term implications for more avoidant individuals and their relationships. (PsycINFO Database Record
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".