Fuzzy Logic Decision Making Model and Risk Early Warning Mechanism in Mental Health Management of Higher Education Students
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a risk indicator system for mental health management of college students that takes individual developmental status, social environment, human-computer interaction, and negative emotions as the first-level indicators, and clarifies the path of obtaining mental health management monitoring data, the weights of the indicators, and the safety warning interval of mental health management.Because of the uncertainties in the mental health management of college students, fuzzy logic is introduced to deal with the uncertainties of environmental changes, student behavior and other factors in the mental health management, and to improve the level of mental health management in colleges and universities.A fuzzy logic-based risk warning model for mental health management of college students is designed.The mental health status of students is further refined by the SCL-90 scale, and the mean score level of each factor of the scale is compared with the youth norm and adult norm.Input the fuzzified student mental health data in the fuzzy logic risk early warning model, and output the risk score of the fuzzy logic model for mental health management of college students.When the set threshold is 60, the fuzzy logic risk early warning model can effectively identify the abnormal values of students' mental health, and the early warning model has practical utility.
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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) | 0.001 | 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