Cost-Effective Predictive Modeling for Student Mental Health Using Readily-Available Data
Bibliographic record
Abstract
The mental health of post-secondary students is a critical public health issue, with alarming rates of psychological distress, suicidal thoughts, and behaviors on university and college campuses. Predictive modeling can be utilized for the analysis of student mental health to better understand current students' mental state. These predictions are often made using large surveys collected from students or participants to use the survey questions as features and then predict based on a target question related to mental health. The expensive nature of collecting data this way can be prohibitive for some institutions, and due to the scale and potential data processing required, the predictions made using those data could be too late for any proactive approaches to tackle the mental health of students. To address this, it is worth investigating the predictive performance of readily available data to predict student mental health as a means of accurately representing an institution's student body. In this paper, we show that readily-available data can be used to predict mental health with competitive accuracy compared to other experiments done in the literature that utilize more expensively collected data with neural network models.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".