How children's anxiety symptoms impact the functioning of the hypothalamus–pituitary–adrenal axis over time: A cross-lagged panel approach using hierarchical linear modeling
Bibliographic record
Abstract
Anxiety symptoms in childhood and adolescence can have a long-term negative impact on mental and physical health. Although studies have shown dysregulation of the hypothalamus-pituitary-adrenal axis is associated with anxiety disorders, it is unclear how and in what direction children's experiences of anxiety symptoms, which include physiological and cognitive-emotional dimensions, impact the functioning of the hypothalamus-pituitary-adrenal axis over time. We hypothesized that higher physiological symptoms would be contemporaneously associated with hypercortisolism, whereas cognitive-emotional symptoms would be more chronic, reflecting traitlike stability, and would predict hypocortisolism over time. One hundred twenty children from the Concordia Longitudinal Risk Research Project were followed in successive data collection waves approximately 3 years apart from childhood through midadolescence. Between ages 10-12 and 13-15, children completed self-report questionnaires of anxiety symptoms and provided salivary cortisol samples at 2-hr intervals over 2 consecutive days. The results from hierarchical linear modeling showed that higher physiological symptoms were concurrently associated with hypercortisolism, involving cortisol levels that remained elevated over the day. In contrast, longitudinal results over the 3 years between data collection waves showed that chronic worry and social concerns predicted hypocortisolism, showing a low and blunted diurnal cortisol profile. These results have implications for broadening our understanding of the links between anxiety, the stress response system, and health across the course of development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".