Nonsuicidal self-injury in street-involved adolescents : identification of risk and protective factors
Bibliographic record
Abstract
Nonsuicidal self-injury (NSSI), or the deliberate, direct, self-inflicted injury to body tissue that occurs in the absence of suicidal intent and developmental disabilities, is a serious and increasingly prevalent health risk among adolescents. Evidence suggests that vulnerable adolescents, such those that are street-involved, are at high risk for negative health outcomes, including NSSI. Using a theoretically and empirically derived model of risk and resilience, this study is the first to identify a broad range of risk and protective factors associated with NSSI. The study involved secondary analysis of data gathered using the Street-Involved Youth Health Survey (SYHS) with a sample of 762 adolescents aged 12-18 across British Columbia. Prevalence of NSSI was 56% and 34% for females and males, respectively, with sexual minority youth three times more likely to report this behaviour. Results from a series of logistic regression analyses revealed different models of risk and protection for males and females. At the multivariate level, the strongest risk factors for females were previous suicide attempt, risky behaviour, experiencing more consequences of substance use, sexual abuse by two or more perpetrators, and maternal problems. For males, the strongest risk factors were previous suicide attempt, risky behaviour, and being victim of relational aggression. The strongest modifiable protective factors for males and females were better emotional health and family connectedness, with school peer relations for males and subjective health status for females also showing significance. Probability profiles created from different combinations of the final set of salient factors highlight not only the multiplicative risk at play in these youths` lives but also the impact of protective factors to offset risk. For boys, with three risk factors (RF) and three protective factors (PF), the likelihood of NSSI ranged from 9% (0-RF, 3-PF) to 90% (3-RF, 0-PF). For girls, with three risk factors and two protective factors, the likelihood of NSSI ranged from 19% (0-RF, 2-PF) to 99% (3-RF, 0-PF). Profiles in this study underscore the value of risk and protection as powerful tools for developing the knowledge base on NSSI and for guiding prevention and intervention efforts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".