P-1007 - Positive maternal mental health: promoting resilience and wellbeing in perinatal women
Bibliographic record
Abstract
Pregnancy and new motherhood are challenging times for families. Promoting positive maternal mental health improves overall health and can prevent mental health problems, which could be detrimental to the woman, her baby, and her family. Since maternal mental well-being is fundamental to the health of her entire family, it is essential that we promote the development and maintenance of positive mental health in perinatal women. Positive mental health is believed to be the optimal way to minimize the risk of mental illness. It is more than the mere absence of mental illness; it also encompasses strategies that will maximize a woman's mental health throughout the perinatal period. Positive mental health also promotes recovery from mental health problems and should be part of every treatment plan. Therefore, it is critical that we help women learn how to deal with the changes associated with pregnancy and new motherhood in ways that promote mental wellbeing and allow them to adapt and enjoy this important period of their life. This presentation explores positive mental health, resilience, optimism, wellbeing, and empowerment in pregnant and postpartum woman. In addition, it provides strategies to promote maternal mental health, specifically, incorporating protective assets for mothers and families. Mental health promotion for pregnant and postpartum women will improve health outcomes for individual women and their families, and will contribute to a more optimistic and thriving society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".