Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Mental illnesses affect almost 15% of the world’s population, with half of the cases emerging before age 14. Improved methods for predicting mental distress among adolescents, particularly in vulnerable populations, are needed. This study utilized traditional machine learning techniques to predict mental health status at age 17. We assessed the correlates of mental health outcomes in a sample of 632 adolescents with general mental distress (i.e., total difficulties score of 17 or higher) at age 11, who participated in the UK Millennium Cohort Study. Predictors measured at ages 11 and 14 were included in the analysis. Mental health status at age 17 was best predicted using a Balanced Random Forest model (AUC 0.75). Explainability techniques enabled the identification of several critical factors, such as school environment, emotional distress, sleep patterns, patience, and social network at ages 11 or 14, which were able to differentiate participants with poor or good mental health outcomes at age 17. Individuals experiencing persistent mental distress between the ages 11 and 17 were most likely to suffer from unhappiness and academic struggles. Our results point to potentially modifiable factors associated with the progression of mental distress in adolescents at high risk. These factors could pave the way for improved early intervention and preventive strategies for vulnerable young people during adolescence.
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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 | 0.001 | 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 it