Multicenter analysis on the non-suicidal self-injury behaviors and related influencing factors—A case study of left-behind children in northeastern Sichuan
Bibliographic record
Abstract
BACKGROUND: Few studies have been conducted focusing on the non-suicidal self-injury (NSSI) incidence rate and influencing factors among left-behind children in northeastern Sichuan, China. In this study, we investigated the incidence rate of the NSSI behaviors, levels of anxiety and depression in left-behind children in northeastern Sichuan, and relevant sociodemographic factors. METHODS: The NSSI behaviors were identified using the Ottawa Self-injury Inventory (Chinese version). Its incidence rate and related influencing factors were evaluated by Cluster sampling; the Depression Anxiety Stress Scale-21 Items (DASS-21) was adopted to assess the levels of anxiety, depression, and stress. RESULTS: A total of 9450 adolescents met the inclusion criteria, including 543 with NSSI behaviors, and the prevalence of NSSI was 5.7 %. There were 3596 left-behind children, and 243 of them had NSSI behaviors, the NSSI incidence in left-behind children was 6.8 %. There were 5854 non-left-behind children, and the NSSI incidence in non-left-behind children was 5.1 %. Left-behind children's NSSI behaviors were remarkably correlated with five risk factors: being female (OR = 2.411, 95%CI = 1.773-3.279), parents' divorce (OR = 1.742, 95%CI = 1.262-2.404), elder age (OR = 1.120, 95%CI = 1.028-1.219), severe depression (OR = 1.212, 95%CI = 1.148-1.281), and high anxiety (OR = 1.170, 95%CI = 1.093-1.251). LIMITATIONS: This is a cross-sectional study, we cannot probe into the causality between NSSI behaviors and corresponding risk factors. Reporting and recalling biases may be unavoidable as questionnaires are performed by self-rating scales and retrospective reports. CONCLUSION: The NSSI behaviors are common in left-behind children in northeastern Sichuan. This study proposes that prevention and intervention measures are necessary for the healthy growth of such children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".