Understanding the Role of Demographics in Emotional Processing: A Study on Alexithymia across University Students
Bibliographic record
Abstract
The purpose of this study was to investigate how university students' demographics affect their alexithymia. Convenient sampling was used to reach a sample size of 482. A demographic questionnaire and the Toronto Alexithymia Scale (TAS-20-UR) were used to collect data. SPSS-24 was used for both descriptive statistics and neural network analysis. Alexithymia is significantly predicted by demographic characteristics, according to the study's normalized importance results. Specifically, the best indicator of alexithymia was the father's occupation (Importance =.172; 100%) followed by birth order (Importance =.144; 83.4%). The influence of parent’s education was the moderate strong (mother's education, Importance =.127; 73.4% and father's education Importance =.124; 71.9%). Alexithymia was also influenced by socioeconomic position (Importance =.081; 47.1%) and number of siblings (Importance =.105; 61%). Age (Importance =.081; 46.9%), family system (Importance =.059; 34.3%), and residential status (Importance =.057; 33.1%) was also altering alexithymia. The least significant variable was gender (Importance =.049; 28.7%). It was determined that the most important demographic predictors of alexithymia were the father's occupation, birth order, and parental education, while gender and residential status had little relevance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".