Fetal Sex, Social Support, and Postpartum Depression
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of prenatal and postnatal social support on the association between fetal sex and postpartum depression (PPD). METHOD: We conducted a prospective cohort study in Changsha, China, between February and September 2007. We first compared the sociodemographic and obstetric characteristics, and the prenatal and postnatal social support between women who gave birth to a female infant and those who gave birth to a male infant. We then examined the association between fetal sex and PPD by following logistic regression models: fetal sex as the independent variable; with adjustment for sociodemographic and obstetric factors; with adjustment for sociodemographic, obstetric factors, and prenatal social support; and with adjustment for sociodemographic, obstetric factors, and postnatal social support. RESULTS: Postnatal social support scores were much lower in women who gave birth to a female infant than in those who gave birth to a male infant. The odds ratio of PPD for women who gave birth to a female infant, as compared with those who gave birth to a male infant, was 3.67 (95% CI 2.31 to 5.84). The increased risk of PPD for women who gave birth to a female infant remained after adjustment for sociodemographic and obstetric factors and prenatal social support, but disappeared after adjustment for postnatal social support score. CONCLUSION: We conclude that increased risk of PPD in Chinese women who give birth to a female infant is caused by lack of social support after childbirth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".