Anxiety Sensitivity Moderates the Association Between Father-Child Relationship Security and Fear Transmission
Bibliographic record
Abstract
Observational fear learning can contribute to the development of fear-related psychopathologies, such as anxiety disorders and post-traumatic stress disorder. Observational fear learning is especially relevant during childhood. Parent-child attachment and anxiety sensitivity modulate fear reactions and fear learning but their impact on observational fear learning has not been investigated. This study investigated how these factors contribute to observational fear learning in children. We examined this question among 55 healthy parent-child dyads. Children (8-12 years old) watched a video of their parent undergoing a direct fear conditioning protocol, where one stimulus (CS+Parent) was paired with a shock and one was not (CS-), and a video of a stranger for whom a different stimulus was reinforced (CS+Stranger). Subsequently, all stimuli were presented to children (without shocks) while skin conductance responses were recorded to evaluate fear levels. Our results showed that children more sensitive to anxiety and who had lower father-child relationship security levels exhibited higher skin conductance responses to the CS+Parent. Our data suggest that the father-child relationship security influences vicarious fear transmission in children who are more sensitive to anxiety. This highlights the importance of the father-child relationship security as a potential modulator of children's vulnerability to fear-related psychopathologies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".