War exposure, post-traumatic stress symptoms and hair cortisol concentrations in Syrian refugee children
Bibliographic record
Abstract
Altered secretion of cortisol, the primary effector of the hypothalamus-pituitary-adrenal axis, has been proposed as a means by which traumatic experiences compromise later mental health. However, despite the popularity of cortisol as a potential biomarker for stress and adversity, findings are inconsistent, and little is known about the impact of war-related trauma on stress physiology of children and adolescents. Here we aimed to evaluate the relationships between war exposure, current living conditions, hair cortisol concentrations (HCC) and post-traumatic stress disorder (PTSD) symptoms in a large cohort of Syrian refugee children and adolescents (6-18 years) and their caregiver. This longitudinal observational study assessed Syrian refugee children and adolescents in two waves, 1 year apart, within informal tented settlements in Lebanon. The relationships between war exposure, time since leaving Syria, PTSD symptoms and HCC were investigated using linear mixed-model regression utilising both waves of data collected (Y1: N = 1574, Y2: N = 923). Hair cortisol concentration was positively, but weakly associated with the number of war-related events experienced. This was limited to those who were at least 12 years old at the time of war exposure. Conversely, HCC decreased with time since leaving Syria. HCC was also associated with PTSD symptoms but not with the quality of their current living conditions. This study revealed that changes to hypothalamic-pituitary-adrenal axis activity may accompany both earlier war exposure and current PTSD symptoms in children and adolescents. Additionally, early adolescence may be a particularly sensitive time in terms of trauma-related changes to the hypothalamic-pituitary-adrenal axis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".