The Developmental Psychopathology of Perinatal Depression: Implications for Psychosocial Treatment Development and Delivery in Pregnancy
Bibliographic record
Abstract
Taking a developmental psychopathology perspective, our objective was to identify ways in which psychosocial treatment of depression during pregnancy may be enhanced. We first consider the state of evidence on psychosocial interventions for antenatal depression, next define key developmental psychopathology concepts that are relevant to antenatal depression, and finally discuss implications for clinical practice and research. We found a limited, but promising, evidence base for effective psychosocial interventions for depression during pregnancy. Examining antenatal depression from a developmental psychopathology perspective revealed suggestions for improving treatment. A developmental psychopathology perspective suggests that treatment of depression during pregnancy may be improved by attention to the continuum of depression, from subclinical to severe major depressive disorder; personalized care based on individual women's pattern of risk and resilience factors and correlated risks; consideration of the potential benefits of treating the couple's relationship, the mother's qualities of parenting, and infants' and children's mental health needs; and, including a detailed understanding of the developmental pathways to antenatal depression for each patient in treatment planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".