The relationship between alexithymia, irrational beliefs, positive and negative emotions with mental disorders
Bibliographic record
Abstract
Introduction: University students, as the future manpower resources, are of high importance for communities. One of the aspects to focus on is their mental health. The present study was conducted to determine the relationship between alexithymia, irrational beliefs, positive and negative emotions with mental disorders in students. Methods: There were 400 students, selected randomly through stratified sampling from different disciplines at Razi University (Kermanshah, Iran). The design of the study was descriptive-correlational. The questionnaires used for collecting data were: Mental Disorders Symptoms (SCL_90), Alexithymia (FTAS-20), Jones Irrational Beliefs tests, and Positive and Negative Syndrome Scale (PANAS-X). Collected data were analyzed using statistical indices including correlation, regression and fundamental correlation. Results: Data analysis indicated that there was a significant relationship between alexithymia, irrational beliefs and negative emotions with mental disorders. There was a negative significant relationship between positive emotions and mental disorders. Alexithymia, irrational beliefs and negative emotions were capable of predicting mental disorders while positive emotions did not have this capacity. Although about 73% of mental disorders were predicted by irrational beliefs, alexithymia, negative and positive emotions, but negative emotions and alexithymia contributed more than other variables. Conclusion: Alexithymia and irrational beliefs play major roles in mental disorders. These variables explain a high variability rate of mental disorders. They are the predisposing factors for mental disorders. They need more attention in clinical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".