The Impact of Alexithymia and Perceived Social Support on Suicidal Probability of Drug Abusers' Patients.
Bibliographic record
Abstract
Background: Alexithymia can cause emotional disturbances and dysregulation, which increases the risk of addictive behaviors and suicidal thoughts in drug abuse patients, which in turn exacerbates emotional dysregulation, and creates a vicious cycle that is difficult to break. Perceived social support can reduce the incidence of suicidal ideation and increase personal self-esteem through the use of social support. Aim of study: to explore the impact of alexithymia and perceived social support on suicidal probability among drug abusers' patients. Subjects and Methods : The study was conducted at addiction clinic in the outpatient clinics of Zagazig university hospitals using descriptive correlational design on137 drug abusers' patients .Data were collected using A structured interview questionnaire, Drug Use Disorder Identification Test, Toronto Alexithymia Scale ,Suicide Probability Scale and Scale of Perceived Social Support. Results: 82.5% of drug abuse patients had alexithymia, 69.3%had moderate level of suicidal risk, 97.8%had drug related problems, and 82.5% were drug dependent. Suicidal probability increased with alexithymia (p=0.014), lower perceived social support (p=0.0001), drug –related problems (p=0.005) and drug dependents (p=0.0001). Treatment duration is positive correlated with suicidal risk (r=0.341), while negative correlated with perceived social support (r=-0.209). Period of addiction is positive correlated with alexithymia (r=0.179).Multivariate analysis explained drug abuse and alexithymia (DDF) dimension were positive predictors of suicide probability .Conversely, perceived social support and alexithymia (EOT) dimension were negative predictors. Conclusion: The majority of the patients have alexithymia and are drug addicts, with a moderate level of suicidal risk and perceived social support. Suicidal risk is associated with drug abuse and alexithymic patients, but not with perceived social support. Recommendations: A psychological intervention program to improve emotional awareness and perceived social support is recommended to prevent drug abuse and suicidal probability. As well, emotion-focused intervention strategies for patients to reduce alexithymia.
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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 | 0.000 | 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) | 0.001 | 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".