Associations of hair cortisol concentration with self-reported measures of stress and mental health-related factors in a pooled database of diverse community samples
Bibliographic record
Abstract
A pooled database from diverse community samples was used to examine the associations of hair cortisol concentration (HCC) with self-reported stress and stress-linked mental health measures, including depression, anxiety, alcohol and drug use, disability and experiences with aggression. As part of innovative research using a mobile laboratory to study community mental health, data were pooled from five sub-studies: a random sample of the general population (n = 70), people who had received treatment for a mental health and/or substance use problem (n = 78), family members of people treated for mental health and/or substance use problems (n = 49), community volunteers who sometimes felt sad or blue or thought they drank too much (n = 83) and young adults in intimate partner relationships (n = 44). All participants completed a computerized questionnaire including standard measures of perceived stress, chronic stress, depression, anxiety, hazardous drinking, tobacco use, prescription drug use, illicit drug use, disability and intimate partner aggression. HCC was significantly associated with use of antidepressants, hazardous drinking, smoking and disability after adjusting for sub-study and potential confounders (sex, body-mass index, use of glucocorticoids and hair dyed). In addition, preliminary analyses suggest a significant curvilinear relationship between HCC and perceived stress; specifically, HCC increased with higher perceived stress but decreased at the highest level of stress. Overall, HCC was associated with mental health-related variables mainly reflecting substance use or experiencing a disability. The relationship between HCC and self-reported stress is unclear and needs further research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".