Impact of alexithymia, speech problems and parental emotion recognition on internalizing and externalizing problems in preschoolers
Bibliographic record
Abstract
BACKGROUND: Alexithymia, characterized by difficulty identifying and describing emotions and an externally oriented thinking style, is a personality trait linked to various mental health issues. Despite its recognized importance, research on alexithymia in early childhood is sparse. This study addresses this gap by investigating alexithymia in preschool-aged children and its correlation with psychopathology, along with parental alexithymia. METHODS: Data were analyzed from 174 parents of preschoolers aged 3 to 6, including 27 children in an interdisciplinary intervention program, all of whom attended regular preschools. Parents filled out online questionnaires assessing their children's alexithymia (Perth Alexithymia Questionnaire-Parent Report) and psychopathology (Strengths and Difficulties Questionnaire), as well as their own alexithymia (Perth Alexithymia Questionnaire) and emotion recognition (Reading Mind in the Eyes Test). Linear multivariable regressions were computed to predict child psychopathology based on both child and parental alexithymia. RESULTS: Preschool children's alexithymia could be predicted by their parents' alexithymia and parents' emotion recognition skills. Internalizing symptomatology could be predicted by overall child alexithymia, whereas externalizing symptomatology was predicted by difficulties describing negative feelings only. Parental alexithymia was linked to both child alexithymia and psychopathology. CONCLUSIONS: The findings provide first evidence of the importance of alexithymia as a possible risk factor in early childhood and contribute to understanding the presentation and role of alexithymia. This could inform future research aimed at investigating the causes, prevention, and intervention strategies for psychopathology in children.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".