Analysis of Sociodemographic, Psychological, and Genetic Factors Contributing to Depressive symptoms in Pre-, Peri- and Postmenopausal Women
Bibliographic record
Abstract
Depressive symptoms that are faced by women in the pre-, peri-, and postmenopausal periods are determined by a wide array of sociodemographic, psychological, and biological variables. The aim of our study was to identify factors that contribute to depressive problems at this stage of life. The study included 815 healthy Polish women aged 45–60 years. The survey part was conducted using the Beck Depression Inventory (BDI), the State–Trait Anxiety Inventory (STAI), the Neuroticism–Extroversion–Openness Five Factor Inventory (NEO-FFI), and a self-developed questionnaire. Genetic analysis was also performed. Depressive symptoms were observed in 25.5% of participants. 70% of the women were postmenopausal. No statistically significant differences in the severity of depressive symptoms were demonstrated with regard to genetic variables (p > 0.05). Reproductive capacity (p < 0.001), employment (p < 0.001), and being married (p < 0.018) were found to reduce the incidence of depressive symptoms. The contribution of personality and anxiety as a trait to depressive symptoms varied. Conclusions: The factors predisposing pre-, peri-, and postmenopausal women to depressive symptoms include lower education, lack of a life partner, unemployment, high anxiety, and neurotic personality. No evidence was found for the contribution of genetic factors to depressive symptoms in the examined women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".