The Despair of Identity Diffusion: Associations With Psychache and Hopelessness and the Indirect Effect of Childhood Emotional Abuse
Bibliographic record
Abstract
Identity diffusion, involving a lack of cohesive personal identity, is a vulnerability factor in affective states-psychache and hopelessness-that often precede suicidality. Traumatic experiences, such as childhood emotional abuse, may limit the capacity to form a stable and coherent sense of self, thereby indirectly heightening the vulnerability to psychache and hopelessness through diffuse identity. The present study examined the relationship between identity diffusion and psychache and hopelessness, along with the indirect effect of perceived childhood emotional abuse. The sample (n = 297) comprised UK-based help-seeking adults recruited online. Eligible participants completed measures of identity diffusion, unbearable psychache, hopelessness and childhood emotional abuse at baseline, with psychache and hopelessness reassessed two months later. Regression analyses revealed that identity diffusion was significantly associated with both psychache and hopelessness over time, even after controlling for baseline levels. Mediation analysis further indicated that identity diffusion had a significant mediating effect on the relationship between childhood emotional abuse and later experiences of psychache and hopelessness. These findings underscore the importance of clinically targeting identity diffusion to help reduce presuicidal affective states, particularly among individuals who experience childhood emotional maltreatment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".