Considering a Simple Strategy for Detection of Women at Risk of Psychological Distress after Childbirth
Bibliographic record
Abstract
BACKGROUND: Postpartum depression is a common, severe, yet often undetected condition. Between 10 and 15 percent of new mothers suffer from depressive disorders in the first year after childbirth. The objective of this study was to investigate whether asking women questions about their daily life constituted a useful strategy to detect women at risk of developing psychological distress after childbirth. METHODS: A prospective study of 330 first- and second-time mothers was conducted. Structured interviews with women were performed at the maternity unit 1 to 2 days after childbirth, and postal questionnaires were sent to participants 5 months later. An interviewer wrote down her perception of the mood of participants, in the form of three short statements, immediately after the interview. This perception was compared with the score of the woman on the General Health Questionnaire scale, which was included in the 5 months' questionnaire. RESULTS: The interviewer's perception of women's mood was significantly associated with the score on the General Health Questionnaire scale 5 months later. Multivariate analysis showed that the interviewer's perception of anxiety was a better predictor of postpartum psychological distress at 5 months than women's answers to questions about their mood before pregnancy and 1 to 2 days after delivery. CONCLUSIONS: Asking the new mother questions about her private and occupational life can be considered as one of many possible ways to improve the identification of women at risk of developing postpartum depression.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".