Research on improving college students' ability to resist frustration from the perspective of positive psychology
Bibliographic record
Abstract
In the new era, China has put forward higher requirements for the comprehensive quality of talents, which requires talents not only to have enough professional ability and professional quality, but also to put forward higher requirements for high-quality talents and other aspects of psychological quality, moral cultivation and thought. New era due to the employment pressure, learning pressure and social pressure, lead to many college students in the process of continuous learning resistance, negative, and so on and so forth, college students' ability to resist setbacks, when the actual face of setbacks cannot quickly self emotional regulation and recovery, which leads to the impact of college students, serious even lead to psychological problems of college students. It is necessary to pay attention to the ability of college students to improve their ability to resist frustration, so as to protect the mental health of college students. Positive psychology is a commonly used teaching method in the field of psychological education. Positive psychology and college students 'anti-frustration ability have some applicability and relevance, so it is a commonly used concept mode in the education of college students' anti-frustration ability. In view of the improvement effect of positive psychology on college students' ability to resist setbacks, relevant personnel need to pay attention to and study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".