Predicting the Social-Emotional Competence Based on Childhood Trauma, Internalized Shame, Disability/Shame Scheme, Cognitive Flexibility, Distress Tolerance and Alexithymia in an Iranian Sample Using Bayesian Regression
Bibliographic record
Abstract
The purpose of this study was to predict Social Emotional Competence based on childhood trauma, internalized shame, disability/shame scheme, cognitive flexibility, distress tolerance, and alexithymia in an Iranian sample using Bayesian regression. The participants in this research were a sample of 326 (85.3% female and 14.7% male) people living in Tehran in 2021 who were selected by convenience sampling through online platforms. The survey assessments included demographic characteristics (age and gender), presence of childhood trauma, social-emotional competence, internalized shame, the Toronto Alexithymia scales, Young's measure of disability/shame together with measures of cognitive flexibility and distress tolerance. The results from Bayesian regression and Bayesian Model Averaging (BMA) indicated that internalized shame, cognitive flexibility and distress tolerance can be predictive of Social Emotional Competence. These results suggested that Social Emotional Competence can be explained by some important personality factors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".