Cultural and socio-economic conditions as factors contributing to chronic stress in sub-Saharan African communities
Bibliographic record
Abstract
Stress is known to contribute to overall health status. Many individuals in sub-Saharan Africa are believed to be stressed about their employment, income, and health. This study aimed to investigate hair cortisol as a biomarker of chronic stress in settlement communities in Kenya. Hair samples were collected from 108 volunteers from settlement communities in Kenya. An enzyme-linked immunosorbent assay technique was used to measure hair cortisol concentrations. In parallel, a health survey was completed. The mean ± SD for the cortisol concentration in the hair of volunteers from the settlement communities in Naivasha was 639 ± 300 ng/g, which was higher than found for a Caucasian reference group (299 ± 110 ng/g; one-way ANOVA, P = 0.0003). There were no differences in hair cortisol concentrations between members of slum settlements adjacent to large floriculture farms in Naivasha (Karagita, Kamere/Kwa Muhia/DCK, and Kasarani) compared with those well-removed from all floriculture in Mogotio (Mogotio and Westlands/Katorongot). However, hair cortisol concentrations were significantly higher in females, divorced volunteers, those who made below minimum wage, and those who reported feeling unsafe collecting water or using sanitation facilities within these 2 settlement groups. We found no evidence for increased chronic stress (measured by hair cortisol content) between members of slum settlements adjacent to versus distant to large floriculture farms. Cultural and socio-economic conditions that prevail in much of sub-Saharan Africa were found to be factors contributing to chronic stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".