Examining the Relationship Between Alexithymia, Loneliness, and Differentiation with Suicidal Thoughts in High School Students
Bibliographic record
Abstract
Objective: The present study aimed to examine the relationship between alexithymia, loneliness, and differentiation with suicidal thoughts in high school students. Materials and Methods: This research is a descriptive correlational study. The statistical population included all female high school students in District 4 of Tabriz during the 2023-2024 academic year (N=4000). Using Krejcie and Morgan's table and multistage cluster random sampling, 351 students were selected as the sample. The data collection tools included the Jackson Differentiation of Self Inventory (2003), the Toronto Alexithymia Scale (1994), the Beck Scale for Suicidal Ideation (1991), and the UCLA Loneliness Scale (1980). Data were analyzed using Pearson correlation coefficient and multiple linear regression analysis with SPSS version 20. Findings: The results indicated a significant positive correlation between alexithymia and loneliness with suicidal thoughts in high school students. Additionally, a significant inverse relationship was found between differentiation and suicidal thoughts in high school students. The multiple regression results also showed that differentiation (beta = -0.38), alexithymia (beta = 0.35), and loneliness (beta = 0.23) could significantly predict changes in suicidal thoughts among high school students, accounting for 52% of the variance in suicidal thoughts. Conclusion: The findings of this study underscore the importance of planning and providing appropriate educational and therapeutic programs aimed at enhancing self-differentiation to reduce suicidal thoughts among students, while also addressing loneliness and alexithymia. The study's results should be communicated to education officials for implementation.
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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 | 0.000 | 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) | 0.001 | 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".