Sedentary Behaviour and Hair Cortisol Amongst Women Living in Socioeconomically Disadvantaged Neighbourhoods: A Cross-Sectional Study
Bibliographic record
Abstract
Women living in socioeconomically disadvantaged neighbourhoods are at heightened risk of experiencing psychological stress. Therefore, identifying potential risk factors for stress is important to support positive mental health. A growing body of research has linked sedentary behaviour with mental ill-health (e.g., depression and anxiety); however, little research has specifically investigated potential linkages between sedentary behaviour and stress. Therefore, the aim of this study was to investigate the association between common types of sedentary behaviour and objectively-measured stress (as measured by hair cortisol levels) amongst women living in socioeconomically disadvantaged neighbourhoods. During 2012-2013, 72 women (aged 18-46 years) living in socioeconomically disadvantaged neighbourhoods self-reported sedentary behaviour (TV viewing, computer use, overall sitting time) and provided hair samples. Hair cortisol levels were measured using enzyme-linked immunosorbent assay. Linear regression models examined cross-sectional associations between sedentary behaviour and hair cortisol levels. There was no association between any type of sedentary behaviour (TV viewing, computer use, or overall sitting time) and hair cortisol levels in either crude or adjusted models. Sedentary behaviour may not be linked to hair cortisol level (stress) in women living in socioeconomically disadvantaged neighbourhoods. Further studies utilising objective measures of both sedentary behaviour and stress are required to confirm these findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".