Scaling Up Patient-Centered Psychological Treatments for Perinatal Depression in the Wake of a Global Pandemic
Bibliographic record
Abstract
There is a call to action to reduce the public health burden of perinatal depression worldwide. The COVID-19 pandemic has further highlighted significant gaps in perinatal mental health care, especially among women who identify as Black, Indigenous, People of Color (BIPOC). While psychotherapeutic (cognitive, behavioral and interpersonal) interventions are endorsed for perinatal mood disorders, barriers to access and uptake contribute to inequitable access to treatment at the population level. To effectively address these barriers and increase the scalability of psychotherapy among perinatal women, we suggest four pragmatic questions to be answered from a patient-centered lens; namely, "who," "what," "how," and "when." Promising avenues include task-sharing among mental health non-specialists, an emphasis on culturally sensitive care, web-based delivery of psychotherapy with some caveats, and a lifespan approach to perinatal mental health. Innovative research efforts are seeking to validate these approaches in diverse contexts across North America and the UK, lending optimism toward scalable and long-term solutions for equitable perinatal mental health 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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".