Predicting alexithymia in adolescents based on early trauma and attitudes toward father and mother
Bibliographic record
Abstract
Introduction: Recent studies show that there is a positive correlation between alexithymia and a wide range of diseases including mood disorders, eating disorders, substance abuse, cardiovascular diseases, diabetes, rheumatoid arthritis, intestinal inflammation, cancer, respiratory diseases, and chronic pains. The aim of this study is to predict alexithymia on the basis of early trauma and attitudes toward mother and father. Â Materials and Methods: In this canonical correlation study in 2012-2013, 300 students (150 girls, 150 boys) were selected via multi stage random sampling in Shiraz high schools. All participants were asked to complete Early Trauma Inventory, Child's Attitude toward Father (CAF) and Mother (CAM) Scales and Toronto Alexithymia Scale (TAS). Data analysis was done using SPSS software version 18 and canonical correlation. Â Results: Structural coefficients showed that the pattern of high scores in difficulty identifying feelings and difficulty describing feelings correlate with the pattern of high scores in early trauma, attitudes toward father and attitudes toward mother (P<0.001).. Therefore, our findings show that the combination of low difficulty identifying feelings and low difficulty describing feelings can probably decrease the likelihood of early trauma and attitudes toward father and mother. Â Â Conclusion: In general, the findings show that early trauma and attitudes toward father and mother can predict difficulty identifying feelings and describing feelings and explain a considerable variance of survival index.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".