Relationship of alexithymia to personality styles in people dependent on psychoactive substance
Bibliographic record
Abstract
The addiction to psychoactive drugs still remains among the relevant research topics. The research herein focuses on the study and analysis of the relations among the alexithymia, and personality styles. The analyses of such psychological constructs may represent valuable views beneficial for the progress in the up-to-date addictology. The article deals with a number of topics, such as, definition of alexithymia, the issue of addictology and personality aspects.. The applied statistical methods are descriptive statistics, factor analysis, non-parametric Spearman's correlation analysis and Mann-Whitney U Test. The reason for choosing the non-parametric statistics has been the conclusion of the normality test pointing at the fact that the acquired data had not complied with the normal distribution assumption. The data collection methods were questionnaires TAS-20 (Toronto Alexithymia Scale) to measure alexithymia, and PSSI (Personality Style and Disorder Inventory). The gross sample under research was represented by 55 probands, namely 14 women and 41 men. The data were collected in the Psychiatric Hospital of Marianna Oranžská in Bílá Voda. The obtained results enabled us to answer the postulated research questions, which were evaluated and the following conclusions were reached: In people addicted to alcohol alexithymia occurs in 41.83 %. In subjects addicted to psychoactive drugs with alexithymia there is a substantial difference in the personality styles of a schizoid, obsessive-compulsive, avoidant, negativistic, borderline, histrionic, and self-defeating type. We believe that in this field of research there is still a huge gap to be filled, and we hope that the research may help to do so by enriching the knowledge with concrete results.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".