Using Fuzzy Comprehensive Evaluation Method to Assess the Implementation Effect of College Students’ Mental Health Education in the New Media Environment
Bibliographic record
Abstract
This paper constructs a comprehensive evaluation system based on the CIPP model, covering multiple dimensions such as input evaluation and outcome evaluation, in order to comprehensively measure the effect of college students' mental health education in the new media environment.In terms of weight determination, the subjective weights are obtained by hierarchical analysis method, then the objective weights of each index are calculated by entropy value method based on the actual data, and then the combination assignment method is used to organically combine the subjective and objective weights to obtain the inal indexes.The relationship matrix was constructed on the basis of a large amount of collected data, and the fuzzy comprehensive evaluation method was used to comprehensively assess the implementation effect of college students' mental health education.The results of the study show that the overall level of the effect of college students' mental health education is good, with the ratings of 79.54 and 78.28 for their mental health knowledge and ideological awareness evaluation, respectively, and that the mastery of mental health methodology and the awareness of proactively seeking psychological help are the main factors affecting the mental health of college students.In addition, the mastery level of college students' mental health practice ability is average (69.52), and there is an obvious de iciency in their theory to practice, which also adds dif iculty to the construction of college students' mental health.Therefore, the fuzzy comprehensive evaluation method can be used to optimize the evaluation system of college students' mental health education in the new media environment.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".