Alexithymia and Coping Strategies: Predictors of Hopelessness?
Bibliographic record
Abstract
Introduction Alexithymic traits and coping strategies may affect the onset and course of many psychiatric conditions. However, their role in determining hopelessness and suicide risk has been not still elucidated. Objectives The present study analyzed the correlations between alexithymia, coping strategies, and hopelessness. Aims We aimed to evaluate whether specific coping strategies and alexithymia may predict hopelessness which is widely considered an independent risk factor for suicide. Methods This is a cross-sectional study conducted on 276 patients (19.9% men, 81.1% women; mean age: 48.1 years, SD: 16.9), of which most with major affective disorders, who were admitted at the Psychiatric Unit of the University of Genoa (Italy). All participants were assessed using the Beck Hopelessness Scale (BHS), Coping Orientations to Problems Experienced (COPE), and Toronto Alexithymia Scale (TAS-20). Results Alexythimic subjects significantly differ from non-alexythimic individuals in terms of substance abuse (χ2 = 23.1; P = .027). According to bivariate analyses, we found a significant correlation between hopelessness and suicidal thoughts/wishes (r = .34; P = .01), humor (r = –.24; P = .05), and behavioural disengagement (r = .205; P = .05). Behavioural disengagement is also a positive predictor of hopelessness (OR = 1.25; 95% CI: 1.03–1.52) while humour is a negative predictor of hopelessness (OR = 0.85; 95% CI: 0.73–0.99). Conclusions Behavioural disengagement needs to be considered a risk factor while humor is a protective factor for suicide. Surprisingly, we found no significant association between alexithymia and hopelessness. Further additional studies are requested to test these exploratory findings in order to more deeply elucidate the role of both alexithymia and coping strategies in suicidal behaviour. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 |
| 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) | 0.003 | 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".