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Record W4411863485 · doi:10.1016/j.scib.2025.06.031

Prevalence and related factors of antenatal depression in 11 provinces and cities of China: a 100,000 population-based study

2025· article· en· W4411863485 on OpenAlexaff
Chen Zhang, Chen-Chi Duan, Han Liu, Hualin Xu, Cheng Li, Jianxia Fan, Hong Li, Xiangjuan Li, Yi Guo, Yeping Wang, Xiufeng Li, Jing Li, Ting Zhang, Yiping You, Hongmei Li, Shuangqi Yang, Xiaoling Tao, Yajuan Xu, Haihong Lao, Li Ming Wen, Yan Zhou, Junying Wang, Yuhua Chen, Diyun Meng, Jingli Zhai, Youchun Ye, Qinwen Zhong, Xiuping Yang, Jing Zhang, Xifeng Wu, Wei Chen, Cindy‐Lee Dennis, Ben W. Mol, Dan Zhang, He-feng Huang, Yanting Wu

Bibliographic record

VenueScience Bulletin · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaShanghai Key Discipline Construction ProjectShanghai Jiao Tong UniversityShanghai Municipal Health CommissionChinese Academy of Medical Sciences Initiative for Innovative MedicineShanghai Clinical Research CenterNational Natural Science Foundation of ChinaShanghai Lingjun Program
KeywordsChinaDepression (economics)GeographyDemographyChinese populationEnvironmental healthMedicinePopulationSociology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.287
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2025
Admission routes1
Has abstractno

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Same venueScience BulletinSame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207