Mother's occupation and sex ratio at birth
Bibliographic record
Abstract
BACKGROUND: Many women are working outside of the home, occupying a multitude of jobs with varying degrees of responsibilities and levels of psychological stress. We investigated whether different job types in women are associated with child sex at birth, with the hypothesis that women in job types, which are categorized as "high psychological stress" jobs, would be more likely to give birth to a daughter than a son, as females are less vulnerable to unfavourable conditions during conception, pregnancy and after parturition, and are less costly to carry to term. METHODS: We investigated the effects of mother's age, maternal and paternal job type (and associated psychological stress levels) and paternal income on sex ratio at birth. Our analyses were based on 16,384 incidences of birth from a six-year (2000 to 2005 inclusive) childbirth dataset from Addenbrooke's Hospital in Cambridge, UK. We obtained a restricted data set from Addenbrooke's hospital with: maternal age, maternal and paternal occupations, and whether or not the child was first-born. RESULTS: Women in job types that were categorized as "high stress" were more likely to give birth to daughters, whereas women in job types that were categorized as "low stress" had equal sex ratios or a slight male bias in offspring. We also investigated whether maternal age, and her partner's income could be associated with reversed offspring sex ratio. We found no association between mother's age, her partner's job stress category or partner income on child sex. However, there was an important interaction between job stress category and partner income in some of the analyses. Partner income appears to attenuate the association between maternal job stress and sex ratios at moderate-income levels, and reverse it at high-income levels. CONCLUSIONS: To our knowledge this is the first report on the association between women's job type stress categories and offspring sex ratio in humans, and the potential mitigating effect of their partners' income.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".