Predicting Social Adjustment in University Students based on Alexithymia and Psychological Vulnerability
Bibliographic record
Abstract
The research has been performed to the aim of predicting social adjustment based on alexithymia and psychological vulnerability of students in Department of Human Sciences of Payam-e Noor University in Sari. Statistical population of the research included all B.A. students from Department of Human Sciences of Payam-e Noor University in Sari in academic year of 2018-19, from among whom 234 individuals have been selected through multi-stage random cluster sampling. To measure research variables, three questionnaires of Toronto alexithymia scale, short form of symptom checklist of mental disorder (SCL-25), and social adjustment of students have been used. Validity and reliability of questionnaires have been respectively checked and confirmed through content validity and Cronbach’s alpha. To analyze data, descriptive statistics, Pearson Correlation Coefficient, and multivariate regression have been applied via SPSS 24. According to the results obtained, alexithymia along with high level of psychological vulnerability are potential sources of affecting social adjustment in students; and, they directly predict students’ social adjustment. So, students’ social adjustment can be predicted based on their alexithymia and psychological vulnerability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".