Intelligent vulnerability assessment system for college students mental health based on few-sample reinforcement...

College students' psychological vulnerability assessment is constrained by data scarcity, high labeling cost, privacy sensitivity, and small, imbalanced samples, and existing frameworks typically treat vulnerability as a static judgment that cannot track how a student's condition changes over time. This study examined whether a few-sample hierarchical grading model combined with meta-reinforcement-learning-based sequential decision-making could address both problems jointly.

A few-sample hierarchical grading model, combining meta-learning and transfer regularization with a prototype-based embedding and ordinal threshold assignment, quantified static risk from a handful of labeled examples per grade, while a sequential risk-identification method formulated dynamic assessment as a Markov decision process solved by a two-stage meta-reinforcement-learning policy. Case studies used a longitudinal, class-imbalanced cohort simulation calibrated to the documented statistics of a mobile-sensing mental-health study (no real participant data were used) and compared the proposed models against threshold-rule, supervised, fine-tuned, few-shot, and non-meta reinforcement-learning baselines, with sensitivity analyses on support-set size, reward weights, discount factor, and regularization weight, plus a regime-switching robustness check.

The proposed grading model attained a macro-F1 of 0.847 with ten labeled examples per grade, over twenty percentage points above a conventional deep-learning classifier, though its edge over other episodic few-shot architectures was small and inconsistent, indicating episodic meta-learning as the main source of this gain. The meta-reinforcement-learning policy achieved a higher alert precision-recall area (0.514 vs. 0.380) than a non-meta baseline and avoided its degenerate always-escalate behavior, but neither the main study nor the regime-switching check showed the composite reward lengthening lead time versus a simpler accuracy-only reward, which matched its lead time at a much lower escalation rate. Sensitivity analyses showed the reward-weight ratio and discount factor shaping the early-identification versus intervention-cost trade-off.

These findings suggest that combining few-sample meta-learning with metareinforcement-learning-based sequential decision-making can improve vulnerability assessment under scarce, evolving data relative to conventional static and non-meta-adapted approaches, with an accuracy-only reward recommended as the practical default. The case studies relied on a calibrated simulation rather than raw longitudinal records, so prospective validation on real campus data under appropriate privacy safeguards is needed before responsible deployment.