Models and key elements of integrated perinatal mental health care: A scoping review
Bibliographic record
Abstract
Perinatal mental illness is a common and significant complication of pregnancy and childbirth. When left untreated, these illnesses are associated with an increased risk of adverse health outcomes for mothers, infants, and families. While early detection and effective management are essential, less than 15% of affected individuals receive timely and appropriate treatment. Integrated care offers a promising approach to addressing complex treatment barriers; however, the core features of integrated perinatal mental health (PMH) care are not well understood. We conducted a scoping review to identify and synthesize evidence on existing models and key elements of integrated PMH, with data extracted according to PRISMA guidelines. The search was conducted across four databases: Ovid MEDLINE, EMBASE, PsycInfo, and CINAHL. We included peer-reviewed articles, published in English between 1990 and 2024, that described models of integrated PMH care. Three reviewers independently screened 4588 articles by title and abstract, with 153 articles selected for full-text review. A total of 45 peer-reviewed articles were retained for analysis. These articles described a wide range of integrated PMH care models, including specialized inpatient units, intensive hospital day programs, outpatient and community clinics, and collaborative and stepped-care frameworks. An analysis of these models identified seven key elements of integrated care: (1) screening, assessment, and triage; (2) integrated care delivery; (3) patient-centred care; (4) a biopsychosocial approach to treatment; (5) PMH-trained clinicians; (6) health promotion and illness prevention; and (7) transition and discharge planning. This evidence suggests that care integration improves the accessibility, continuity, and quality of PMH care. Integrated models of care can take many forms with positive impacts on perinatal individuals and their families. Research is needed to establish consensus on the key elements of integrated care to support implementation.
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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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".