Bibliographic record
Abstract
Many present studies have investigated the risk factors of depression in menopausal women; some paid attention to their living environment, and others focused on women themselves. This study will discuss the inner and outer effects that may affect depression in menopausal women in the US between 2000 and 2004. The data used in this study is from SWAN dataset. In this dataset, all participants took a 2-round cross-sectional survey from seven research centers across the US in 1994. 9 variables were selected, and eight were included in a logistic regression model, with whether the women are in depression being the dependent variable. The result(estimated coefficients, p-value) shows that the stress level(0.288, 0.000), if the participant was still upset about the illness of their family(0.657; 0.011), middle(0.504; 0.000) and high irritability level(0.991; 0.027) have a significant positive correlation with risk of depression; a little stress of being a mom(- 0.363; 0.040), extremely stressful of being a mom(-0.552; 0.048), their age(-0.052; 0.038) and if their menopausal status is posted by bilateral salpingo(-0.880; 0.011) have a significant negative correlation with risk of depression. However, further investigations on newer and broader populations need to be done to confirm the result more accurately.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".