Students’ Problems Presented upon Attending in The University Counseling Centers of Ahvaz Jundishapur University of Medical Sciences
Bibliographic record
Abstract
Introduction:University counseling centers, as a section of vice chancellery for students and cultural affairs, serves for improving students’ physical and psychosocial well-being. The purpose of this study was to investigate students’ problems making them attend counseling centers in Ahvaz Jundishapur University of Medical Sciences. Methods:In a descriptive correlation study, the students’ problems making them visit counseling centers of Ahvaz Jundishapur University of Medical Science were reviewed for a year interval (2011-2012). The data was extracted from the records (647 student files) in the counseling centers using a form and a database was developed. Descriptive statistics (frequency, mean, and standard deviation) and inferential statistics (chi-square test, independent t test, and Pearson correlation coefficient) were used in data analysis. Results:Out of 3200 students in the university, 647 (20%) visited University Counseling Centers at least once. Visitors were mostly female students (73.13% vs. 26.87%, χ2=12.92 and p=0.02). The frequency of causes bringing students to counseling center were: educational and academic problems for 164 (25.6%) students, psychological-emotional problems in 140 (21%), personal problems in 140 (21.7%), marital problems in 133 (20.7%), and family problems in 67 (10.4%). The chi-square analysis indicated that significant differences exist among the causes for referring students by sex, marital status, and not being Ahwaz inhabitant(p<0.05). Conclusion: Less than a quarter of students had attended counseling centers and these visits were mostly due to educational and psycho-emotional problems. Therefore it is recommended to continuously assess students’ counseling needs and conduct workshops to help resolve their most prevalent problems.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".