Empirically Derived Subgroups of Self‐Injurious Thoughts and Behavior: Application of Latent Class Analysis
Bibliographic record
Abstract
Latent class analysis was applied to the sample data to identify homogenous subtypes or classes of self-injurious thoughts and behavior (SITB) based on indicators indexing suicide ideation, suicide gesture, suicide attempt, thoughts of nonsuicidal self-injury (NSSI), and NSSI behavior. Analyses were based on a sample of 1,809 healthy adults. Associations between the emergent latent classes and demographic, psychological, and clinical characteristics were assessed. Two clinically relevant subtypes were identified, in addition to a class who reported few SITBs, and were labeled as follows: low SITBs (25.8%), NSSI and ideation (25%), and suicidal behavior (29.2%). Several unique differences between the latent classes and external measures emerged. For instance, those belonging to the NSSI and ideation class compared with the suicidal behavior class reported lower levels of entrapment, burdensomeness, fearlessness about death, exposure to the attempted suicide or self-injury of family members and close friends, and higher levels of goal disengagement and acute agitation. SITBs are best explained by three homogenous subgroups that display quantitative and qualitative differences. Profiling the behavioral and cognitive components of suicidal and nonsuicidal self-injury is potentially useful as a first step in developing tailored intervention and treatment programs.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".