Language Strategies to Reduce Anxiety of At-risk Children Deprived of Parental Care
Bibliographic record
Abstract
The present study examines language impact on anxiety in at-risk children deprived of parental care. Bulgarianchildren without parents (n=40) divided into 3 age groups (aged 7-10; aged 11-14; and aged 15-17) embedintervention accomplished by four volunteers using four interaction strategies: normal voice and positive language;high tone and positive language; normal tone and negative language, and high tone and negative language.Surveillance has been conducted subjecting anxiety reactions of children measured in 10 indicators: diffidence,dependence, dissatisfaction, reliance, insecurity (for self-assessment anxiety); inadequacy, inactivity,non-communication, inability to seek help, and lack of empathy (for interpersonal anxiety). The present study resultsdemonstrate that linguistic signs have higher influence than paralinguistic cues on children’s behaviour and the useof negative language would increase anxiety in children to a greater extent when the tone is normal while raising thetone would enhance self-assessment and interpersonal anxiety of children with emotional disorders. Balanced use ofnegative and positive language combination with a different tone in different situations would increase socialfunctioning of the child. The use of different strategies for interaction by specialists – with normal and high tone,encouragement or reprimand should be tailored to the specifics of the child as well as to the relevant skills to bedeveloped.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".