Role of Living Conditions and Socioenvironmental Factors on Chronotype in Adolescents
Bibliographic record
Abstract
An individual’s chronotype, defined as the preference for rest and activity at different times of the day, is linked to several physiological and psychological outcomes. Research on environmental determinants of chronotype has focussed mostly on geographic location, whereas other socioenvironmental determinants have been neglected. We aimed to investigate the association between other previously unrecognized socioenvironmental factors and chronotypes in adolescents. We analysed data of 1916 Bengali adolescents (aged between 13–14 years, 47% girls). Chronotype was determined by the reduced morningness–eveningness questionnaire (rMEQ), and socioenvironmental factors were identified through a structured questionnaire. Associations were analysed using multinomial logistic regression models. Our findings demonstrated that living in urban areas, the presence of a smoker at home, and higher parental education were associated with a higher evening activity (eveningness), while the use of biomass cooking media (compared to liquefied petroleum gas) and assisting parents in farming were associated with higher morningness in adolescents. This is the first study to identify the association between previously unrecognized socioenvironmental factors and chronotypes delineating the interaction between environment and sleep in adolescents and might help the parents to understand the importance of a proper sleep-activity rhythm of their kids through a comprehensive understanding of their surrounding environment and other factors.
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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) | 0.001 | 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".