Partner support and maternal depression in the context of the Iowa floods.
Bibliographic record
Abstract
A systematic investigation of the role of prenatal partner support in perinatal maternal depression was conducted. Separate facets of partner support were examined (i.e., received support and support adequacy) and a multidimensional model of support was applied to investigate the effects of distinct types of support (i.e., informational, physical comfort, emotional/esteem, and tangible support). Both main and stress-buffering models of partner support were tested in the context of prenatal maternal stress resulting from exposure to a natural disaster. Questionnaire data were analyzed from 145 partnered women using growth curve analytic techniques. Results indicate that received support interacts with maternal flood stress during pregnancy to weaken the association between stress and trajectories of maternal depression from pregnancy to 30 months postpartum. Support adequacy did not interact with stress, but was associated with levels of depressive symptoms controlling for maternal stress and received support. Results demonstrate the distinct roles of various facets and types of support for a more refined explanatory model of prenatal partner support and perinatal maternal depression. Results inform both main effect and stress buffering models of partner support as they apply to the etiology of perinatal maternal depression, and highlight the importance of promoting partner support during pregnancy that matches support preferences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".