Conditions of negation formation in children of early age with down syndrome
Bibliographic record
Abstract
Introduction According to Vygotsky, children with special needs follow the same trajectory of development as normally developing children, although some of the skills can be observed in a later period. This statement can be implemeted to the children with Down syndrome. The number of such children in Russia is around 25 thousand. Objectives The aim is to study the conditions of negation formation in children with Down syndrome. Methods The sample consisted of 22 dyads of children with Down syndrome of 24-36 months old and their mothers. The research methods included: parents’ questionnaire; analysis of problematic situations; Tkacheva’s inventory Parent’s Psychological Type; Varga &Stolin Inventory of Parental Attitude; Toronto Alexithymia Scale, Bass-Darky Hostility Questionnaire, Leonhard-Schmieschek Test, Spielberger’s Test Anxiety Inventory. Results Firstly, we have studied how a child expresses his or her negative reaction: whether he or she uses a gesture or a sound for “no” or reacts with the whole body. According to those results we have divided the sample into two groups and then have compared them. The research shows the connection between mother’s aggressiveness and formation of the child’s negation reaction (gesture/sound or the whole body) as well as differences in the level of alexithymia and anxiety: all the characteristics are lower in the first group. Conclusions Mothers of the children with Down syndrome demonstrate a high and a medium level of anxiety. However, the mothers of the children who expresses negation with a gesture/sound show a lower anxiety level comparing with the mothers whose children react with the whole body.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".