Perinatal mental health and COVID-19: Navigating a way forward
Bibliographic record
Abstract
The COVID-19 pandemic and its aftermath have increased pre-existing inequalities and risk factors for mental disorders in general, but perinatal mental disorders are of particular concern. They are already underdiagnosed and undertreated, and this has been magnified by the pandemic. Access to services (both psychiatric and obstetric) has been reduced, and in-person contact has been restricted because of the increased risks. Rates of perinatal anxiety and depressive symptoms have increased. In the face of these challenges, clear guidance in perinatal mental health is needed for patients and clinicians. However, a systematic search of the available resources showed only a small amount of guidance from a few countries, with a focus on the acute phase of the pandemic rather than the challenges of new variants and variable rates of infection. Telepsychiatry offers advantages during times of restricted social contact and also as an additional route for accessing a wide range of digital technologies. While there is a strong evidence base for general telepsychiatry, the particular issues in perinatal mental health need further examination. Clinicians will need expertise and training to navigate a hybrid model, flexibly combining in person and remote assessments according to risk, clinical need and individual patient preferences. There are also wider issues of care planning in the context of varying infection rates, restrictions and vaccination access in different countries. Clinicians will need to focus on prevention, treatment, risk assessment and symptom monitoring, but there will also need to be an urgent and coordinated focus on guidance and planning across all organisations involved in 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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".