Inside out – do adverse childhood experiences predict nonsuicidal self-injury?
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI) is defined as behavior that is self-directed and deliberate, \nresulting in injury or potential injury to oneself without suicidal intent, although it consistently \ncorrelates with suicidality. Common forms of NSSI include cutting, burning, scratching, \nbanging, hitting, biting etc. Meta-analysis showed that overall childhood maltreatment is \nassociated with NSSI, especially in the case of childhood emotional neglect or emotional abuse. \nHighly lethal self-harm was associated with childhood physical peer victimization, sexual \nabuse, emotional abuse, and emotional neglect. The NSSI questionnaire designed for this study \nwas based on several questionnaires such as Deliberate Self-Harm Inventory (DSHI), Inventory \nof Statements About Self-Injury (ISAS), Ottawa Self-Injury Inventory (OSI) and Self-Harm \nBehaviour Questionnaire (SHBQ). The NSSI used in this survey contains 12 items with joint \nbinary (yes or no) and numeric (how many times) scales. On all items, respondents provided \nanswers with respect to two time periods: before and after the age of 18 (laws in Serbia restrict \nrights of persons under the age of 18 and some of those are related to potentially risky behaviors \nsuch as rights regarding alcohol purchase). Overall, approximately 4% of respondents reported \nNSSI at least once in lifetime, out of which 3.6% reported NSSI at least once before the age of 18 and 1.8% at least once after the age of 18. Since this is a form of behaviour is typical for \nyounger adolescents, as expected, the NSSI is more prevalent before the age of 18 (2 = 17.225, \np < .01). The correlation between the frequency of NSSI before and after the age of 18 is \nr = 0.73 (p < .01), while the correlation between suicide attempts and NSSI was Φ = 0.25 \n(p < .01). When it comes to the prediction of NSSI that occurred after the age of 18, ACE scores \nwere not significant predictors. However, regression analysis showed about 9% of the variance \nof the NSSI before the age of 18 can be related to ACEs. Specifically, three types of ACEs \nwere significant predictors of NSSI: sexual abuse (β = 0.16, p < .01), incarceration of a family \nmember (β = 0.11, p = 0.051) and abuse of father by the partner (β = 0.15, p = 0.012). Available \ndata suggest that at least one part of the variance can be ascribed to the ACEs. Therefore, \nemphasis should be put on fostering coping strategies in adolescents that would lead to \ndiminishing negative consequences of ACEs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".