Depressive Symptomatology in the Immediate Postnatal Period: Identifying Maternal Characteristics Related to True- and False-Positive Screening Scores
Bibliographic record
Abstract
OBJECTIVE: To determine whether true- and false-positive postnatal depression screening scores can be distinguished during the early postpartum period by examining characteristic differences between 2 groups: 1) women with depressive symptomatology at 1 week postpartum who continue to exhibit symptoms at 8 weeks postpartum, compared with those who do not; and 2) women with depressive symptomatology at 8 weeks postpartum who previously exhibited symptoms at 1 week postpartum, compared with those who did not. METHOD: As part of a longitudinal postpartum depression study, a population-based sample of 594 women completed mailed questionnaires at 1, 4, and 8 weeks postpartum. RESULTS: Among women with depressive symptomatology at 1 week postpartum, diverse variables distinguished between those whose symptoms persisted or remitted at 8 weeks. These variables included recent immigrant status, psychiatric history, premenstrual symptoms, vulnerable personality, low self-esteem, child abuse history, and insufficient support. Variables that distinguished between women with depressive symptomatology at 8 weeks postpartum who previously exhibited symptoms at 1 week postpartum and those who did not included vulnerable personality, life stressors, perceived stress, insufficient support, and partner conflict. CONCLUSIONS: To address both the benefits and potential harms of early screening, positive screening scores on the Edinburgh Postnatal Depression Scale should also include an assessment of each individual woman's risk for postpartum depression and (or) chronic major 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".