Maternal mental health: a shared care approach
Bibliographic record
Abstract
BACKGROUND: Maternal mental health problems affect up to 20% of women, with potentially deleterious effects to the mother and family. To address this serious problem, a Maternal Mental Health Program (MMHP) using a shared care approach was developed. A shared care approach can promote an efficient use of limited specialized maternal mental health services, strengthen collaboration between the maternal mental health care team and primary care physicians, increase access to maternal mental health care services, and promote primary care provider competence in treating maternal mental health problems. AIM: The purpose of this research was to evaluate the impact of a MMHP using a shared care approach on maternal anxiety and depression symptoms of participants, the satisfaction of women and referring physicians, and whether the program met the intents of shared care approach (such as quick consultation, increased knowledge, and confidence of primary care physicians). METHODS: We used a pre and post cross-sectional study design to evaluate women's depression and anxiety symptoms and the satisfaction of women and their primary care health provider with the program. Findings Depression and anxiety symptoms significantly improved with involvement with the program. Women and physicians reported high levels of satisfaction with the program. Physician knowledge and confidence treating maternal mental health problems improved. CONCLUSIONS: Shared care can be an effective and efficient way to provide maternal mental health care in primary health care settings where resources are limited.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".