Attachment Styles and Suicidal Thoughts and Behaviors: A Meta-Analysis
Bibliographic record
Abstract
Introduction: This meta-analysis examined the association between dimensional and categorical conceptualizations of attachment styles and suicidal thoughts and behaviors (STBs). Methods: Random-effects meta-analysis was conducted to examine the associations between categorical secure attachment, categorical insecure attachment, and insecure attachment dimensions and STBs. Methodological moderators were also explored. This protocol was registered in PROSPERO (CRD42020152604). Results: Systematic search for articles published by December 2020 returned 58 eligible studies and 159 cross-sectional effects. Secure attachment was inversely associated with suicidal thoughts and not associated with suicide attempts. All categorical insecure attachment styles were positively associated with suicidal thoughts. Only fearful and preoccupied attachment were associated with risk for suicide attempts. Dimensional attachment anxiety was more strongly associated with suicidal thoughts and attempts than dimensional attachment avoidance. Discussion: Overall, attachment styles characterized by high attachment anxiety were associated with greatest vulnerability to STBs. Longitudinal studies are needed to better understand the association between attachment insecurity and STBs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.041 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".