Investigating the Association between Season of Birth and Symptoms of Depression and Anxiety in Adults
Bibliographic record
Abstract
Background: The season of birth exposes a fetus to varying environmental and developmental conditions which may influence health outcomes after birth. The influence of season of birth has been observed in neuropsychiatric disorders and chronic health conditions. However, research on the association between season of birth and common mental health disorders is currently limited. This study sought to fill this gap by investigating the association between season of birth and symptoms of depression and anxiety in adults using a survey-based cross-sectional research design. Methods: Participants for this study (n=303) were primarily women (65%) with a mean age of 26 years old. Season of birth was assessed as Winter, Spring, Summer or Fall based on birth month. Depression symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) and anxiety symptoms were assessed using the Generalized Anxiety Disorder-7 (GAD-7) scale. Other variables included include biological sex, age, income, and latitude. Findings: Mental health conditions were common among participants, with 84% and 66% of participants experiencing symptoms of depression and anxiety, respectively. Season of birth was not associated with anxiety symptoms However, while males born during summer had a higher risk of developing depression symptoms, there was no observed association among females. Interpretation: This study provided further evidence supporting the association between season of birth and emergence of adulthood depression symptoms, particularly in relation to sex. Future studies should investigate the biological sex-specific mechanisms that underlie mental health outcomes in relation to the developmental conditions experienced at different times of the year.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".