The association between subjective socioeconomic status and depressive symptoms in women of reproductive age: the chain mediating effects of marital satisfaction and well-being
Bibliographic record
Abstract
Objective The underlying mechanism between subjective socioeconomic status (SSS) and depressive symptoms among women of reproductive age in China is not fully. This study aims to explore the mediating roles of marital satisfaction and well-being in the association between SSS and depressive symptoms. Methods A total of 4,219 women of reproductive age were selected from the 2022 China Family Panel Studies. Data related to SSS, marital satisfaction, well-being, and depressive symptoms were extracted. Spearman rank regression and bootstrap methods were used to analyze the chain mediation effects of SSS, marital satisfaction, well-being, and depressive symptoms. Results (1) SSS, marital satisfaction, well-being, and depressive symptoms were significantly correlated ( p < 0.01). (2) SSS directly affected depressive symptoms ( β = −0.1092, p < 0.001). (3) Marital satisfaction ( β = −0.0873, p < 0.001) and well-being ( β = −0.0867, p < 0.001) each played a mediating role in the effect of SSS on depressive symptoms. (4) Marital satisfaction and well-being played a chain mediating role in the association between SSS and depressive symptoms in women of reproductive age ( β = −0.0703, p < 0.001). Conclusion There is a chain mediation effect between SSS, marital satisfaction, well-being, and depressive symptoms in women of reproductive age. Improvement in SSS can enhance marital satisfaction, which in turn increases well-being, ultimately alleviating depressive symptoms in women of reproductive age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".