Evaluation of alexithymia, anger and anxiety depression levels in smokers
Bibliographic record
Abstract
Objective Smoking is a major mental health concern due to its addictive nature and its status as the leading preventable cause of premature death worldwide. This study aimed to examine the levels of alexithymia, anger, anxiety, and depression in smokers compared to nonsmokers.Materials and Methods This case-control study involved 176 smokers from a Smoking Cessation Clinic and 175 age- and gender-matched nonsmokers. Participants completed assessments using the Toronto Alexithymia Scale (TAS-20), State-Trait Anger Expression Inventory (STAXI), and Hospital Anxiety and Depression Scale (HADS). Nicotine dependence in smokers was measured using the Fagerström Nicotine Dependency Test. Statistical analysis was performed using SPSS 22.0.Findings Significant differences were observed between smokers and nonsmokers in TAS, TAS-1, TAS-2, HADS-A, and HADS-D scores. Smokers exhibited higher levels of alexithymia, anger expression, and anxiety, while nonsmokers demonstrated better anger control. Additionally, addiction levels in smokers were associated with variations in TAS, TAS-1, TAS-2, TAS-3, STAXI scores (SA, AC, AI, AO), and HADS-A, highlighting a complex interplay between addiction, psychological factors, and smoking habits.Result This study establishes a link between smoking status, addiction levels, and elevated alexithymia, anger, anxiety, and depression. The findings underscore the psychological impact of smoking, contributing valuable insights for mental health interventions in individuals with smoking habits.
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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 | 0.001 |
| 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".