Maternal attachment security modulates the relationship between vulnerability to anxiety and attentional bias to threat in healthy children
Bibliographic record
Abstract
This study aimed to investigate whether attentional bias to threat, commonly observed in clinically anxious children, also manifests in healthy children, potentially aiding the early detection of at-risk individuals. Additionally, it sought to explore the moderating role of parent-child attachment security on the association between vulnerability factors (anxiety sensitivity, intolerance of uncertainty, perseverative cognitions) as indicators of vulnerability to anxiety, and attentional bias towards threat in healthy children. A total of 95 children aged 8 to 12 years completed the Visual Search Task to assess attentional bias. Vulnerability to anxiety was measured using a composite score derived from the Childhood Anxiety Sensitivity Index, Intolerance of Uncertainty Scale for Children, and Perseverative Thinking Questionnaire. Parent-child attachment security was assessed using the Security Scale-Child Self-Report. Analyses revealed that higher vulnerability to anxiety was associated with faster detection of anger-related stimuli compared to neutral ones, and this association was further influenced by high maternal security. These findings in healthy children suggest an interaction between specific factors related to anxiety vulnerability and the security of the mother-child relationship, leading to cognitive patterns resembling those seen in clinically anxious individuals. These results hold promise for early identification of children at risk of developing anxiety disorders.
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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 | 0.000 | 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".