Do I feel sadness, fear or both? Comparing self-reported alexithymia and emotional task-performance in children with many or few somatic complaints
Bibliographic record
Abstract
Children with many somatic complaints seem to report problems with emotion identification and communication ('alexithymia'). The aim of this study was to verify whether children with somatic complaints do indeed show signs of alexithymia. We compared 35 children (M age = 10.99, SD = 13 months) with many somatic complaints with 34 children (M age = 11.03, SD = 12 months) reporting few complaints on the basis of a self-report alexithymia scale and tasks that require the skill to identify and communicate emotions: an emotional attention task, a structured interview about own emotions, and a mixed-emotion task. Children were also asked about the intensity of the reported emotions. Compared to children with few complaints, children with many complaints seemed to have higher self-reports of alexithymia. However, these results were explained by difficulty in communicating negative internal states and experiencing indefinable internal states, rather than difficulty in identifying emotions. In addition, children with many complaints reported higher intensities of fear and sadness. The children did not differ in their attention to emotions or causes of emotions. Children with many somatic complaints more often described previous emotional experiences and showed better abilities in identifying multiple emotions. Children with many somatic complaints thus show more negative emotional processing, but the alexithymia-hypothesis was unsupported.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".