Relationship between Homesickness and Test Anxiety in Non-Native Students of Shiraz University of Medical Sciences International Branch in the Clinical and Physiopathology Course In 2013
Bibliographic record
Abstract
INTRODUCTION: Anxiety is an emotional and physiological response to the internal felling of overall danger that is easily resolved. The aim of this study has been to determine the relationship between exam anxiety and the feeling of homesickness among non-native students. METHODOLOGY: The present study is cross-sectional and the subjects in this study are 80 non-native male and female PhD candidates in clinical and physiopathology majors in 2013 academic year that have been evaluated with the help of Persian homesickness questionnaire and Sarason's test anxiety questionnaire and the data was analyzed using Pearson's correlation coefficient. RESULTS: With regard to the Pearson's correlation coefficient there is a significant and reverse relationship between the desire to return to home and exam anxiety (r=0.0344, p=0.004) and there is a significant and reverse relationship between the Compatibility and exam anxiety (r=0.428, p<0.0001) and there is a significant and direct relationship between the feeling of alone and exam anxiety (r=0.888, p<0.0001). DISCUSSION & CONCLUSION: There is a significant relationship between the feeling of homesickness and exam anxiety and the mental health of non-native students will be deteriorated by the feeling of homesickness and anxiety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".