Development and validation of a predictive model for suicidal thoughts and behaviors among freshmen
Bibliographic record
Abstract
BACKGROUND: There are fewer studies on prospective predictors of first-time suicidal thoughts and behaviors (STB) among first-year university students and fewer studies prospectively identifying and screening for those at high risk of suicide among college students. This study assessed the impact of prospective baseline variables on the risk of new STB onset among first-year university students over two years and developed a multivariate risk prediction model. METHODS: 4,560 first-year university students (38.4% males, mean age:18.34) from China participated and completed this prospective cohort study over a three-year period from 2018 to 2020. LASSO regression, and logistic regression models under resilient networks, were used for risk predictor variable screening and final prediction model building. Independent validation sets were used for external validation of the models. Independent validation sets were used for external validation of the models. Area Under the Curve (AUC), accuracy, F1 scores, and Hosmer-Lemeshow test metrics were used to evaluate the model performance. RESULTS: The incidence rates of suicidal thoughts, suicidal behaviors, and STB within two years were 4.89%,1.03%, and 4.96%, respectively. Predictors in the final model included females, always solo activity, bigotry under pressure, socially oriented perfectionism, drinking to relieve stress, autonomy attitude, poorer parental marriage satisfaction, maternal emotional warmth, perceived others social support, and number of lifetime severe traumatic events. The predictive model had an AUC of 0.738 (95% CI: 0.697-0.780) for predictive accuracy in the training dataset as well as 0.710 (95% CI: 0.657-0.763) for predictive accuracy in the validation dataset, which represents a high degree of model discrimination. CONCLUSION: Based on this predictive model of suicidal thoughts and behaviors, this study may help to assess and screen college students at risk for STB and develop suicide prevention strategies for at-risk populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".