MétaCan
Menu
Back to cohort

Perinatal depression and suicidal behaviour: the need for timely intervention

2025· article· en· W4408897272 on OpenAlexaboutno aff
Gulshan Rathore, Kanchan Sharma, Nisha Yadav

Bibliographic record

VenueInternational Journal of Reproduction Contraception Obstetrics and Gynecology · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDepression (economics)Intervention (counseling)PsychiatryMedical emergency

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.323
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Reproduction Contraception Obstetrics and GynecologySame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207