Published online 2015 September 01. Research Article Metacognition and Body Image in Predicting Alexithymia in Substance
Bibliographic record
Abstract
Background: Substance dependency is one of the biggest problems and worries of the world. It stunts the growth of society and causes various problems such as reduction in public health, increase in mortality, rise in social and domestic traumas, loss of educational and occupational opportunities, involvement with the judicial system, and development of the substance-abuse cycle. Objectives: The aim of this study was to determine the role of metacognition and body image in predicting alexithymia in substance abusers. Patients and Methods: The research sample included addicts (males and females aged 10 to 70 years) who referred to the addiction treatment and counseling centers of three Iranian cities of Zahedan, Sari, and Neyriz. Participants were selected by random sampling. The metacognitive strategy questionnaire (MCQ-30), physical self-description questionnaire (PSDQ), and Toronto alexithymia scale (TAS-20) were used for data collection. The hypotheses were tested using the Pearson’s correlation method and regression analysis. Results: According to the results of the current study, the highest correlation was between alexithymia and the cognitive awareness subscale (r = 0.305; P < 0.01).There was no significant correlation between alexithymia and body image. Based on the multiple regression analysis, the three predictors explained 11 % of the variance (R2 = 0. 11, F = 3.981; P < 0.01). Cognitive awareness significantly predicted 9 % of
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.437 | 0.106 |
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".