The association between social support and postpartum post-traumatic stress disorder
Bibliographic record
Abstract
BACKGROUND: Postpartum post-traumatic stress disorder (PTSD) is a debilitating condition that can arise following childbirth. Despite a growing body of research on postpartum mental health, the relationship between social support and postpartum PTSD remains unclear. This study aimed to assess the association between social support and postpartum PTSD. METHODS: A prospective cohort study was conducted at a tertiary hospital in Guangdong province of China between November 2022 and April 2023. Eligible mothers were assessed for social support using the Social Support Rating Scale (SSRS) at three days postpartum and for PTSD using the Perinatal Post-Traumatic Stress Disorder Questionnaire (PPQ) at 42 days postpartum. The association between social support and postpartum PTSD was analyzed using multiple linear and log-binomial regression, with adjustments for potential confounders. RESULTS: Forty-six of 560 (8.2%) mothers developed PTSD within 42 days postpartum. Scores for subjective support (β=-0.319, P < 0.001), objective support (β=-0.327, P < 0.001), support availability (β=-0.285, P < 0.001), and overall social support score (β=-0.428, P < 0.001) were inversely associated with PTSD scores. Compared to mothers in the 1st quartile of the overall social support score, those in the 2nd, 3rd, and 4th quartiles had adjusted relative risks of 0.39 (95% confidence interval [CI]: 0.21-0.74), 0.20 (95% CI: 0.09-0.45), and 0.10 (95% CI: 0.03-0.33), respectively, of developing PTSD. An inverse linear trend in the risk of PTSD was observed with increasing social support (P-trend < 0.001). CONCLUSIONS: Social support may have a protective effect against postpartum PTSD, with practical implications for interventions targeting various dimensions of support.
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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 | 0.003 |
| 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) | 0.002 | 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".