International best-practice models for perinatal and infant mental health care – a scoping review
Bibliographic record
Abstract
Background: Perinatal mental illnesses (PMI) affect up to 20% of women and 10% of men during pregnancy and in the first year after the birth of the child. Perinatal mental illness contributes significantly to maternal mortality and adverse neonatal, infant, and child outcomes. Because of the high prevalence and the impact of PMI on both the parents and the infant, there is an urgent need for rapid and effective care. The aim of this scoping review was to identify comprehensive evidence-based guidelines and care models for the prevention and treatment of PMI and summarize their common characteristics. Methods: We searched manually in several databases and on websites of relevant institutions and contacted experts. We included guidelines and guidance documents based on pre-defined inclusion criteria. Results: We identified six relevant guidelines and care models from four countries (United Kingdom, Ireland, Canada, Australia). The identified documents highlight the need for integrated care models (including prevention, early identification, counseling, treatment), clear referral pathways, stepped-care approaches and multi-professional, coordinated networks. Conclusions: The 'ideal' care model should consider not only the mental health of the mother, but also that of the father/co-parent and the children, as well as the parent-infant relationship. The results from this scoping review can be used for further discussion and as decision support for designing, developing, and implementing perinatal and infant mental health (PIMH) care.
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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 | 0.045 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".