Hair cortisol concentrations in war-affected adolescents: A prospective intervention trial
Bibliographic record
Abstract
Temporal examinations of the biological signature of stress or trauma in war-affected populations are seldom undertaken. Moreover, few studies have examined whether stress biomarkers track biological sensitivity to brief interventions targeting the improvement of psychosocial wellbeing. Our study is the first to prospectively examine, in war-affected adolescents, the associations between hair cortisol concentrations (HCC) and self-reports of stress, insecurity, posttraumatic reactions, and lifetime trauma. We conducted a randomized controlled trial to test the impact of an 8-week intervention based on profound stress attunement. We collected data for a gender-balanced sample of 733 Syrian refugee (n = 411) and Jordanian non-refugee (n = 322) adolescents (12-18 years), at three time-points. We used growth mixture models to classify cortisol trajectories, and growth models to evaluate intervention impact on stress physiology. We observed three trajectories of HCC: hypersecretion, medium secretion, and hyposecretion (9.6%, 87.5% and 2.9% of the cohort, respectively). For every one percent increase in levels of insecurity, adolescents were 0.02 times more likely to have a trajectory of hypersecretion (95% CI: 1.00, 1.03, p = 0.01). For each additional symptom of posttraumatic stress reported, they were 0.07 times less likely to show hyposecretion (95% CI: 0.89, 0.98, p = 0.01). Indeed, stronger posttraumatic stress reactions were associated with a pattern of within-individual cortisol dysregulation and medium secretion. Overall, HCC decreased by a third in response to the intervention (95% CI: -0.19, -0.03, p = 0.01). While the intervention decreased HCC for youth with hypersecretion and medium secretion, it increased HCC for youth with hyposecretion (95% CI: 0.22, 1.16, p = 0.004), relative to controls. This suggests a beneficial regularization of cortisol levels, corroborating self-reports of improved psychosocial wellbeing. We did not find evidence to suggest that gender, resilience, or posttraumatic stress disorder influenced the strength or direction of responses to the intervention. This robust impact evaluation exemplifies the utility of biomarkers for tracking physiological changes in response to interventions over time. It enhances the understanding of trajectories of endocrine response in adverse environments and patterns of stress responsivity to ecological improvement.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".