Identity Pathology and Emptiness as Novel Predictors of Suicidal Ideation
Bibliographic record
Abstract
Emptiness and identity pathology are understudied clinical constructs that overlap, co-occur, and predict suicidal ideation (SI). However, specific risk pathways have yet to be formally tested. AIM: We examined whether identity pathology was indirectly associated with future SI via emptiness, and tested impulsivity and emotion dysregulation as moderators. METHODS: Participants (N = 251) completed baseline questionnaires assessing SI, borderline personality disorder symptoms, emotion dysregulation, and impulsivity, and SI 2 months later. RESULTS: Identity pathology was indirectly associated with future SI via emptiness, controlling for baseline SI (β = 0.15, Bootstrap 95% CI = [0.06, 0.24]). There was a two-way interaction between emptiness and both poor use of emotion regulation strategies (β = 0.06, p < 0.001) and impulsive lack of premeditation (β = 0.09, p = 0.03) predicting SI. CONCLUSION: Those with greater identity pathology were more likely to experience emptiness, which was in turn associated with future SI. Participants who felt empty were also more likely to experience SI when they also reported an inability to use emotion regulation strategies and a tendency to act without considering the consequences. We provide preliminary support for an untested risk pathway for SI, highlighting the need to further study these important 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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".