Pathways to depressive symptoms in Chinese pregnant women and their influence on delivery approach: a qualitative comparative analysis
Bibliographic record
Abstract
The aim of this study was to apply complexity theory to explain and understand how risk factors combined in complex ways, eventually leading to a high prevalence of depressive symptoms among pregnant women. We also aimed to evaluate whether depressive symptoms affected delivery approach. The study had a longitudinal design and was conducted between May and September 2017. A total of 481 pregnant women were recruited to participate and completed closed-end surveys at two distinct times: during prenatal care at the hospital after 26 weeks of pregnancy and 1 to 4 weeks after delivery. This study identified eleven different pathways that led to an increase in depressive symptoms. Each pathway differentiated the effects of different influencing factors. Among the 481 pregnant women, 128 (26.6%) had cesarean deliveries without medical indications. Although depressive symptoms could affect delivery approach, it was not the most important factor. Surprisingly, the first production emerged as the key factor determining delivery mode. This study was innovative in that it examined which factors and which combinations of factors were necessary for the development of depressive symptoms. Additionally, this study provided a better understanding of the mechanisms underlying the choice of cesarean section without medical indications.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".