Role of Alexithymia and Self-Concealment in Predicting Readiness to Change Among Patients with Substance Use Disorders
Bibliographic record
Abstract
Background: Substance use disorders can cause serious harm and manifest as psychological, physical, or legal issues. Drug abuse may be used as an alternative strategy since people struggle to manage their emotion regulation problem. Hence, it is hindering their ability to change as a consequence. Aim: to assess the role of alexithymia and self-concealment in predicting readiness to change among patients with substance use disorders. Subjects and method: Design: A descriptive correlational design was utilized in this study. Setting: The study was conducted at neurology, psychiatry and neurosurgery center, Tanta university. Subjects: A purposive sample of 107 patients with substance use disorder was chosen to be subjects for this study. Tools: Sociodemographic and clinical data questionnaire, Toronto Alexithymia Scale (TAS-20), Self-Concealment Scale and Readiness to Change Questionnaire [Treatment Version]: RCQ[TV]. Results: 74.8 % of study subjects had high and moderate degree of alexithymia. Also, 78.5% of them had a high and moderate degree of self-concealment. Regarding readiness to change, 52.3% of the study subjects have merely mild degree of readiness to change. Alexithymia acts as a predictor of readiness to change among patients with substance use disorders. Conclusion: Patients with substance use disorders are often less aware of their emotions and hide their negative experiences from others. Subsequently, this is affecting their willingness to modify the current state of abuse. Recommendation: Improving realization of emotions and emotional management is an important part of the treatment of patients with substance use disorders.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".