Developing consensus to enhance perinatal mental health through a model of integrated care: Delphi study
Bibliographic record
Abstract
Perinatal mental illness is an important public health issue, with one in five birthing persons experiencing clinically significant symptoms of anxiety and/or depression during pregnancy or the postpartum period. The purpose of this study was to develop a consensus-based model of integrated perinatal mental health care to enhance service delivery and improve parent and family outcomes. We conducted a three-round Delphi study using online surveys to reach consensus (≥75% agreement) on key domains and indicators of integrated perinatal mental health care. We invited modifications to indicators and domains during each round and shared a summary of results with participants following rounds one and two. Descriptive statistics were generated for quantitative data and a thematic analysis of qualitative data was undertaken. Study participants included professional experts in perinatal mental health (e.g., clinicians, researchers) (n = 36) and people with lived experience of perinatal mental illness within the past 5 years from across Canada (e.g., patients, family members) (n = 11). Consensus was reached and all nine domains of the proposed model for integrated perinatal mental health care were retained. Qualitative results informed the modification of indicators and development of an additional domain and indicators capturing the need for antiracist, culturally safe care. The development of an integrated model of perinatal mental health benefitted from diverse expertise to guide the focus of included domains and indicators. Engaging in a consensus-building process helps to create the conditions for change within health services.
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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 | 0.200 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".