ALEXITHYMIA AND ITS RELATIONSHIP WITH PHYSICAL COMPLAINTS AND EMOTIONAL COMPETENCY IN CHILDREN AND ADOLESCENTS
Bibliographic record
Abstract
Objectives : The aim of this investigation was to study alexithymia and its relationship with physical complaints, emotional competency and mood disorders in children and adolescents. Method : 593 (308 girls and 285 boys) elementary school children in the city of Shiraz were selected using multi-stage cluster sampling. Participants completed the Alexithymia Questionnaire for Children, Mood List for Children and Somatic Complaint List. The validity of the instruments was determined through calculating correlation between subscales with each other and the total scale, and reliability was determined using Cronbach's alpha. Results indicated a satisfactory and high reliability and validity of the instruments used in this study. Data were analyzed using analysis of variance and regression analysis. Results : Alexithymia showed positive association with physical complaints and negative emotions, and negative association with happiness. Gender differences were also significant, and girls had higher scores averages in comparison with boys. Conclusion : Difficulty in identifying feelings and external oriented thinking had the highest and lowest predictive powers respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".