Associations of menstrual characteristics with non-suicidal self-injury: results from six universities in China
Bibliographic record
Abstract
BACKGROUND: The relationships of menstrual characteristics with non-suicidal self-injury (NSSI), a potential precursor to suicidal behaviors, have rarely been explored. Therefore, we aimed to examine the associations between menstrual characteristics with NSSI among college students in China. METHODS: A cross-sectional study was conducted among students from six universities randomly chosen from 57 universities in Shaanxi province, China, using a multistage, random cluster sampling method. Menstrual characteristics including menarche age, menstrual cycle length, and menstrual cycle regularity were evaluated using a self-designed questionnaire, and NSSI was assessed using the Chinese adaptation of Ottawa Self-Injury Inventory. Binary logistic regression models were employed to examine the relationships between menstrual characteristics and NSSI. RESULTS: A total of 12,192 female students were included in this study. The prevalence of early menarche (≤ 11years), prolonged menstrual cycles (≥ 32 days), and irregular menstrual cycles was 6.7%, 10.2%, and 13.2%, respectively. After adjusting for potential confounders, participants with early menarche (OR, 1.38; 95% CI, 1.03–1.83), prolonged menstrual cycles (OR, 1.57; 95% CI, 1.23-2.00), and irregular menstrual cycles (OR, 1.48; 95% CI, 1.17–1.86) had significantly increased odds of engaging in NSSI in the past 12 months. Moreover, the likelihood of engaging in NSSI increased as the score of menstrual problems increased (P for trend < 0.001). CONCLUSIONS: Our findings suggest that early menarche, prolonged and irregular menstrual cycles were associated with elevated likelihood of engaging in NSSI. These results emphasize the need for early awareness and monitoring by families, teachers, and healthcare professionals to address the potential adverse associations between menstrual difficulties and NSSI.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".