The effects of social factors on anxiety by logistic regression analysis in undergraduate students
Bibliographic record
Abstract
Objective To explore the prevalence of anxiety and its related social factors in undergraduate student, in order to provide scientific evidence for the prevention of anxiety in undergraduate. Methods The cluster random sampling was adopted for this study, and 965 college students from Zhengzhou University were investigated by Personal Report of Communication Apprehension, Simplified Coping Style Questionnaire, Toronto Alexithymia Scale, Perceived Social Support Scale and Self-Rating Anxiety Scale. Results ①The anxiety detection rate of respondents was 19.3%. There were significant differences in the detection rate and severity of anxiety between genders(Male: 27.0%, Female: 10.2%) and grades(Freshman: 16.4%, Junior: 23.8%) in the college students(P 0.05). ②The scores of communication apprehension((69.31±12.32), (65.25±12.56)), positive coping style((20.84±5.10), (23.99±5.18)), negative coping style((11.03±4.15), (09.18±3.96)), Toronto alexithymia((74.97±6.93), (70.31±7.98)) and perceived social support((53.14±5.78), (57.02±5.79)) of the anxiety group were significantly different with those of the normal group (P<0.05). ③The significant relationship was found among anxiety emotions and gender, positive coping style, negative coping style, Toronto alexithymia and perceived social support by using Logistic regression analysis (P<0.05). Conclusions The prevalence of anxiety in undergraduate students is relatively high and it is related with multiple factors. In addition, social support, alexithymia and coping style have a close correlation with anxiety in undergraduates. Key words: Anxiety; Alexithymia; Social support; Logistic regression analysis
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".