Predicting of Suicide Ideation in Individuals with Obsessive-Compulsive Tendencies: The Roles of Alexithymia, Responsibility Attitude, Positive/Negative Affect
Bibliographic record
Abstract
Background and Objectives: The aim of the present study was to investigate the relationship among alexithymia, responsibility attitude, positive and negative affect, and suicidal ideation in people with obsessive tendencies. Material and Methods: The study sample was comprised of 400 students from Tabriz University from whom 151 students with an obsession score higher than the cut-off point were selected randomly as people with obsession tendency. Toronto Alexithymia Scale (TAS-20), responsibility attitude scale (RAS), PANAS Positive and Negative Affect Schedule, Yale-Brown Obsessive-Compulsive Scale and Beck Scale for Suicidal Ideation (BSSI) were used to collect the data. The data were analyzed using Pearson product-moment correlation method and hierarchical regression. Results: The hierarchical regression results showed that controlling demographic variables, alexithymia variable as overall, difficulty identifying feelings subscale, difficulty describing feelings subscale and negative affect can predict suicide. Responsibility and positive affect variables and externally-oriented thinking in alexithymia could not predict the dependent variable. Conclusion: Based on these findings, we can conclude that difficulty identifying feelings, difficulty describing feelings and negative affect are the main determinants for suicide in people with obsessive tendencies.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".