Perinatal depression and suicidal behaviour: the need for timely intervention
Bibliographic record
Abstract
This review examines perinatal depression, including antenatal (pregnancy-related) and postnatal (after childbirth) depression. It highlights their prevalence, risk factors, symptoms, and impacts on women and families, such as premature birth and significant maternal mental health issues. Causes include environmental stressors, genetic predisposition, and hormonal changes. The review distinguishes between temporary "baby blues" and prolonged postnatal depression influenced by social, psychological, and biological factors. Risk factors include negative family dynamics, a history of mental health issues, and lack of social support. Emphasizing the need for timely intervention and comprehensive mental health care, this review used a comprehensive search strategy across databases like PubMed, Google Scholar, Scopus, and more. Keywords related to perinatal depression were used for screening abstracts and titles, with full-text articles assessed for eligibility. Quality was evaluated using tools like the Newcastle-Ottawa scale (NOS) and the critical appraisal skills programme (CASP). Findings highlight the importance of regular mental health screenings, psychotherapeutic approaches, pharmacological treatments, and robust support systems. Understanding the interactions between biological, psychological, and social factors in perinatal depression is crucial for improving maternal and fetal health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".