Characteristics and predictive factors of non-suicidal self-injury in patients with schizophrenia spectrum disorders
Bibliographic record
Abstract
Objective: We aimed to evaluate the characteristics and functions of non-suicidal self-injury (NSSI) in patients with schizophrenia spectrum disorders (SSD) and investigate predictive factors of NSSI. Methods: One hundred and two patients, aged between 18-65 with a diagnosis of SSD according to DSM-5 criteria, who were in remission were consecutively recruited to the study. Lifetime NSSI was assessed using the Inventory of Statements About Self-injury (ISAS). Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale (CDS), Suicide Probability Scale (SPS), Dissociation Scale (DIS-Q), Schedule for Assessing the Three Components of Insight (SAI), and Barratt Impulsivity Scale-11 (BIS-11) were administered to all patients who participated in the study. Logistic regression analysis was conducted to predict NSSI. Results: The prevalence of NSSI was 31.4% in our sample. ‘Self-cutting’ was the most common type (26.1%), and ‘affect regulation’ was the most common function of NSSI. 65.6% of the NSSI (+) group preferred to be alone while self-injurious behavior occurred. The time between the onset of feelings of an urge to injure self and the onset of NSSI was mostly less than three hours (46.9%). The significant predictors of NSSI were the previous history of suicide attempts (OR=2.693, p=0.040, 95% CI: 1.048-6.921) and greater severity of depressive symptoms (OR=1.216, p=0.001, 95% CI: 1.081-1.367). The previous history of suicide attempts was associated approximately threefold increase in the risk of NSSI. The probability of suicide was higher among patients with NSSI than patients without NSSI. Conclusion: Approximately 1/3 of the patients with SSD have NSSI. The results of our study indicate that patients with severe depressive symptoms and a history of previous suicide attempts are more at risk of injuring themselves and that the probability of suicide is higher in patients with NSSI than patients without NSSI. The reciprocal relationship of NSSI with suicide underlines the necessity of careful investigation for both clinical situations in this patient group. Assessment of NSSI should be a part of standard suicide risk assessment of SSD patients. Effective treatment of affective symptoms would help to reduce the risk of NSSI.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".