Using hair biomarkers to examine social-emotional resilience in adolescence: A feasibility study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it