Perinatal Maternal Depressive Symptoms as an Issue for Population Health
Bibliographic record
Abstract
The importance of maternal depression for child outcomes is well established, and impairments in psychosocial function and parenting are as severe in women with high subsyndromal levels of depressive symptoms as they are in women with clinical depression. The author conducted a systematic review that explored the association between maternal depressive symptoms and child neurodevelopmental outcomes, including in neuroimaging studies. The results strongly suggest that the influences of maternal depressive symptoms operate across a continuum to influence child outcomes, implying that maternal depression may appropriately be considered an issue of population health. This conclusion is strengthened by recent findings that reveal distinct influences of positive maternal mental health on parenting and child outcomes. [AJP AT 175: Remembering Our Past As We Envision Our Future April 1851: Fleetwood Churchill, "On the Mental Disorders of Pregnancy and Childbed" "Women affected with any degree of mental derangement during pregnancy are more disposed than others to puerperal mania. But the serious character of these attacks is even deepened by the fact, abundantly established, that the evil is not limited to the mother. Not only may organic diseases of the body be transmitted to the infant, but a predisposition to insanity, thus multiplying the distress in a most alarming ratio." (Am J Psychiatry 1851; 7:297-317 )].
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".