Winter sleep extension and fragmentation in a South African agropastoral community
Bibliographic record
Abstract
OBJECTIVES: To examine seasonal sleep variation and assess the effects of gender, age, and environmental variables (Wet-Bulb Globe Temperature, moonlight, sunrise and sunset times) on sleep in a rural agropastoral community in South Africa with gender division of labor. METHODS: We collected actigraphy data from 114 participants (83 men, 31 women, 4750 nights) during summer and winter seasons in 2023. We used Bayesian hierarchical regression models to investigate drivers of sleep duration and quality. RESULTS: Total Sleep Time was longer in winter (7.26 hours, SD = 1.0) compared to summer (6.40 hours, SD = 0.88), but so were Fragmentation Index and Wake After Sleep Onset. Higher Wet-Bulb Globe Temperature was associated with shorter Total Sleep Time, higher Fragmentation Index, and lower Sleep Efficiency. Greater moon illumination was correlated with shorter Total Sleep Time and reduced Fragmentation Index and Wake After Sleep Onset. Age was positively correlated with Total Sleep Time and Fragmentation Index among men, and older individuals had earlier sleep onset and offset than younger individuals. Compared to women, men had shorter and more disturbed sleep, especially in the winter, and were more impacted by Wet-Bulb Globe Temperature. CONCLUSIONS: Sleep during the winter season was longer but more fragmented and of lower quality compared to the summer. Seasonal differences in extrinsic weather conditions and perceived risks operated on preexisting gendered labor and sleep disparities to drive seasonal sleep variation in this community. Future research should consider the disproportionate effects that environmental variables can have on sleep outcomes for different groups.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".