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 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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".