Evaluation of Two versus Six Psychometric Test Batteries for Digital Screening of University Students: Findings from a Multicentric Study
Bibliographic record
Abstract
Abstract Background: University students are at heightened risk for stress, depression, and anxiety. Screening using multidimensional psychometric batteries can identify students requiring intervention but may be time-consuming and burdensome. This study evaluated whether a brief two-test battery assessing stress and psychiatric symptoms provides referral outcomes comparable to a comprehensive six-test battery (Multidimensional Assessment for Student Stress [MASS]). Methodology: A multicentric, cross-sectional study recruited 600 undergraduate students from three Indian institutions. Participants completed the MASS battery digitally. Referral decisions for counseling or psychiatry were based on algorithmic scoring of stress and psychiatric symptom severity. Concordance between the two-test and six-test batteries was assessed, and diagnostic accuracy was evaluated using sensitivity, specificity, and Cohen’s k. Results: Moderate stress was most prevalent (43.3%), with severe psychiatric symptoms present in 13.3% of students. Overall, 29.2% were referred to counseling and 20% to psychiatry. Referral patterns between the six-test and two-test batteries did not differ significantly (c² =0.38, P = 0.54). Sensitivity and specificity were 84% and 76% for stress-based referrals, and 81% and 72% for psychiatric symptom-based referrals, with substantial agreement between digital assessment and clinical evaluation (Cohen’s k 0.70 for stress; 0.68 for psychiatric symptoms). Conclusions: The two-test battery provides referral outcomes equivalent to the six-test MASS battery, effectively identifying students requiring counseling or psychiatric support. Brief, targeted screening offers a practical, scalable, and reliable alternative for university mental health programs, balancing efficiency with clinical accuracy.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".