Alexithymia in People With Recurrent Suicide Attempts
Bibliographic record
Abstract
Abstract. Background: Alexithymia, an inability to identify or describe emotions, is associated with suicidality yet the correlation with single or repeated suicide attempts is less clear. Aims: We aimed to assess the modifiability of alexithymia following a group psychosocial intervention focused on improving emotional literacy in those with a history of recurrent suicide attempts (RSA). Method: A total of 169 participants with self-reported RSA completed pre- and postgroup assessments of a 20-week group therapy intervention. Questionnaires assessed alexithymia, depression, impulsivity, and hopelessness; the Toronto Alexithymia Scale (TAS-20) was the primary outcome. Data were analyzed using multiple imputation. Results: Participants had on average 7.8 lifetime suicide attempts, 73% were female, and 16.6% had a >13-point reduction in TAS-20 scores after 20 weeks. Directed acyclic graph (DAG) analysis demonstrated significant relationships between alexithymia, depression, hopelessness, problem-solving, and satisfaction with life. Age of onset of suicidality was the only factor predictive of postintervention TAS-20 score in univariate linear regression. Limitations: The study limitations were its sample size, insufficient resources, and missing data. Conclusion: A change in TAS scores indicated that alexithymia can be a modifiable treatment target. Being able to identify and describe feelings may lead to improvement in depression, hopelessness, problem-solving, and satisfaction with life in this population.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".