Using hair biomarkers to examine social-emotional resilience in adolescence: A feasibility study
Bibliographic record
Abstract
Background: The SKY Schools Program combines breath-based techniques and a social-emotional learning curriculum. We examined its effects on objective physiological biomarkers, including hair cortisol (HCC, chronic stress measure) and hair oxytocin (HOC, social affiliation measure), as well as behavioral (youth risk behaviors) and mental health outcomes (anxiety, depression). Methods: The SKY Schools program was adapted for post-pandemic restrictions (i.e., staff shortages, no lessons requiring writing, limited weekly follow-ups) and implemented among 7th grade students (daily in-person 40-min sessions for three weeks during physical education classes). Longitudinal assessments were obtained at baseline (T1, February 2022, N = 21), post-intervention (T2, June 2022, N = 20), and follow-up (T3, December 2022, N = 18). Results: Most of our sample was male (67 %), Hispanic (62 %), and lived in low-income (<$100K) households (75 %). Students reported fewer poor mental health days at follow-up (Friedman test p < 0.01). Log-normal (Ln)-HCC (p < 0.01) were higher post-intervention vs. baseline (median 1.81 (IQR 1.63-2.46) vs. 1.60 (0.91-1.85)) and lower at follow-up (1.23; IQR: 0.64-1.50), with HCC in more students moving into the adaptive range (25th-75th percentile). Ln-HOC (p = 0.04) were higher post-intervention vs. baseline (1.78 (1.54-2.26) vs. 1.50 (0.81-1.70)). Conclusions: This study uniquely evaluated the impact of the SKY intervention on hair cortisol (HCC) and hair oxytocin concentrations (HOC), which are objective, physiological measures of chronic stress and social affiliation. Results suggest that SKY may improve social affiliation and possibly HPA-axis regulation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".