The Practice of Positive Psychological Interventions on Students' Well-Being in the Model of Academy System—the Case of Xi'an Eurasian National University
Bibliographic record
Abstract
The academy system model is effective in the context of education in the era of all-round education, and has positive significance for the cultivation of students' practical ability, innovation ability and social adaptability. But the problems of identity deprivation, time fragmentation, complexity of interpersonal relationships and other problems that appear under the model also bring challenges to student growth, if there is no timely guidance students are prone to low belonging, weak sense of happiness, confused anxiety and other mental health problems, which put forward higher requirements for student care education.The academy system model is effective in the context of all-round education, and has positive significance for the cultivation of students' practical ability, innovation ability and social adaptability.We first investigated the basic information, positive psychological qualities and the current state of well-being of 4374 students in Xi'an Eurasian National University, and then conducted a positive psychological intervention for 480 students using the method of Stochastic intervention experiments.The results of the experiment showed that positive psychological levels and well-being indices were improved through positive psychological interventions.The experimental results showed that students' positive psychological level and happiness index were improved through positive psychological intervention. This provides new ideas for the reform and innovation of the Academy System of caring education model.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".