Posttraumatic Stress Disorder after Pregnancy, Labor, and Delivery
Bibliographic record
Abstract
OBJECTIVES: Other studies of posttraumatic stress disorder (PTSD) after birth did not include questions about prior traumatic life events. This study sought to determine if a difficult birth was associated with symptoms of PTSD as well as considering sociodemographics, history of violence, depression, social support, and traumatic life events. METHODS: New mothers were recruited on the postpartum ward of six Toronto-area hospitals (n = 253) and were interviewed by telephone 8-10 weeks postpartum (n = 200). We dichotomized the postpartum stress (PTS) into high PTS (answered "yes" to 3 or more items) or low PTS (answered "yes" to 0-2 items). We calculated the odds ratios between difficult birth, other factors, and the binary PTS variable. RESULTS: Results of multivariable logistic regression revealed that no factor suggestive of a difficult birth was significantly related to high PTS scores, except having two or more maternal complications (odds ratio [OR] = 4.0, 95% confidence interval [CI] = 1.3-12.8). Other independent predictors of high PTS scores were depression during pregnancy (OR = 18.9, 95% CI = 5.8-62.4), having two or more traumatic life events (OR = 3.2, 95% CI = 1.2-8.3), being Canadian born (OR = 3.2, 95% CI = 1.3-8.1), and having higher household income (lowest income group, OR = 0.1, 95% CI = 0.02-0.5), intermediate income group OR = 0.4, 95% CI = 0.2-0.8). CONCLUSIONS: In this study, postpartum stress symptoms appeared to be related more to stressful life events and depression than to pregnancy, labor, and delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".