Meta‐analytic evidence that attachment insecurity is associated with less frequent experiences of discrete positive emotions
Bibliographic record
Abstract
OBJECTIVE: Individual differences in attachment insecurity can have important implications for experiences of positive emotions. However, existing research on the link between attachment insecurity and positive emotional experiences has typically used a composite measure of positive emotions, overlooking the potential importance of differentiating discrete emotions. METHOD: We conducted a meta-analysis of 10 cross-sectional samples (N = 3215), examining how attachment insecurity is associated with self-reported frequency of experiencing positive emotions, with a distinction made between more social (i.e., love and gratitude) and less social (i.e., peace and awe or curiosity) positive emotions. RESULTS: High (vs. low) levels of both attachment anxiety and avoidance were associated with less frequent experience of positive emotions regardless of their social relevance. When analyzing each emotion separately, we found that attachment anxiety showed negative relations to all emotions except gratitude. Attachment avoidance was negatively associated with all emotions, and the link was even stronger with love (vs. peace, awe, or curiosity). Additional analyses of daily diary data revealed that attachment anxiety and avoidance were also negatively associated with daily experiences of positive emotions, regardless of social relevance. CONCLUSION: Our results underscore the need to further investigate the mechanisms underlying insecure individuals' blunted positive emotional experiences.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".