A Review on University Students’ Resilience and Levels of Social Exclusion and Forgiveness
Bibliographic record
Abstract
The aim of this research is to review the relationship between university students’ resilience and levels of social exclusion and forgiveness. Study group of the research includes 355–206 (58%) female and 149 (42%) male–students who marked at least one item in Risk Factors Determination List. This study is a correlational survey model. The Resilience Scale, The Risk Factors Determination List, The Social Exclusion Scale for Adolescents and Forgiveness Scale are used as data collection tools. Pearson Product-Moment Correlation coefficient and Multiple Linear Regression Analysis are used in data analysis. In the wake of correlation analysis, a significant relationship cannot be found between resilience and forgiveness level. A negative and significant relationship is found between resilience and exclusion and negligence sub-dimensions of social exclusion. In the wake of regression analysis, sub-dimensions of social exclusion predict resilience. In order to increase the resilience of university students, rejection by their friends should be minimized, and in order to prevent individuals from being exposed to social exclusion, communication skills can be improved. Social support, which is among the protective factors of resilience, has an important place in life of university students. Therefore, social activities that every student can participate in can be hold.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".