Hair cortisol concentration is unaffected by basic military training, but related to sociodemographic and environmental factors
Bibliographic record
Abstract
The analysis of hair cortisol concentrations (HCC) is a promising new biomarker for retrospective measurement of chronic stress. The effect of basic military training (BMT) on chronic stress has not yet been reported. The aim of this study was to investigate the effect of 10-week BMT on HCC, while further exploring the role of known and novel covariates. Young healthy male recruits of the Swiss Army participated twice, 10 weeks apart, in data collection (1st examination: n = 177; 2nd examination: n = 105). On two occasions, we assessed HCC, perceived stress and different candidate variables that may affect HCC (e.g. socioeconomic status, meteorological data). Military training increased perceived stress from the first to the second examination, but did not affect HCC. In line with this, there was no correlation between HCC and perceived stress ratings. This could be interpreted as a missing influence of mainly physical stress (e.g. exercise) on HCC. In contrast, significant correlations were found between HCC and ambient temperature, humidity and education. Future studies should control for meteorological data and educational status when examining HCC.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".