Profiles of Psychological Adjustment to Divorce and Separation: Associations With Attachment Insecurity, Forgiveness of the Former Partner, and Emotion Regulation Difficulties
Bibliographic record
Abstract
Divorce and separation represent the dissolution of one of the most significant attachment bonds during adulthood. Previous research has shown that divorced individuals often face heightened mental health challenges. However, this overarching view, focusing on average effects, fails to capture the diverse responses to this life transition, and the identification of profiles of psychological adaptation to divorce-separation remains limited. The present cross-sectional study aimed to identify latent profiles of psychological adaptation to divorce and separation using a person-centered approach. A sample of 938 Chilean adult participants completed specific measures of psychological adaptation and mental health indicators. Latent profile analysis revealed five distinct profiles: two with positive outcomes and three experiencing persistent difficulties. Factors such as attachment insecurity, forgiveness of the former partner, and emotion regulation difficulties were linked to profile membership, along with demographic and divorce-related variables. These findings offer valuable insights to tailor support services for individuals navigating divorce or separation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".