Negative emotions, childbirth pain, perinatal dissociation and self-efficacy as predictors of postpartum posttraumatic stress symptoms
Bibliographic record
Abstract
Objective: The aim of the study was to assess the contribution of negative emotions, childbirth pain, perinatal dissociation, and feelings of self-efficacy to the development of posttraumatic stress disorder (PTSD) symptoms following childbirth. Patients and methods: A prospective longitudinal study was carried out on 98 women from the south of France area. Four questionnaires were completed at 2–3 days postpartum: the Peritraumatic Emotions List (PEL), the French version of the McGill Pain Questionnaire, the Peritraumatic Dissociative Experience Questionnaire (PDEQ) and the Childbirth Self-efficacy Inventory (CBSEI). The Impact of Event Scale-Revised (IES-R) assessing posttraumatic stress symptoms was also completed 6 weeks after delivery. Results: Pain and negative emotions were significant predictors of the intensity of posttraumatic stress symptoms at 6 weeks postpartum. Although higher levels of pain contribute to increased PSTD symptoms, and higher negative emotion also contributes to PTSD symptoms, the effect of pain on PSTD is stronger when there are high levels of negative emotion. Discussion and conclusion: Our findings highlight that pain, negative emotions and their interaction were significant predictors of posttraumatic stress symptoms and confirm the importance of developing more specific treatments focusing on support and prevention.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".