Correlates of depressive symptoms in late middle-aged Taiwanese women: findings from the 2009 Taiwan National Health Interview Survey
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that depressive symptoms in middle-aged women were associated with a number of factors such as climacteric symptoms. Nevertheless, studies based on population-based data with a wide range of potential correlates are still scarce. Therefore, the aim of this study was to investigate the correlates of depressive symptoms in late middle-aged Taiwanese women using data from a nationally-representative, population-based survey. METHODS: Women aged 50.0-65.0 years were identified from the dataset of the 2009 Taiwan National Health Interview Survey. The outcome measure was depressive symptoms in the past week, evaluated using the Center for Epidemiologic Studies Short Depression Scale (CES-D 10) with a cut-off score of 10 or greater. Univariate and multiple logistic regression analyses were used to evaluate the correlates of depressive symptoms. RESULTS: The mean age of the 533 respondents was 56.7 years. Depressive symptoms were present in 53 respondents (9.9%). Multiple logistic regression analysis revealed that an education level of elementary school or below (adjusted odds ratio [AOR] = 3.19, P = 0.003), nulliparity (AOR = 8.10, P = 0.001), living alone (AOR = 5.47, P = 0.003), never having worked (AOR = 4.14, P = 0.008), lack of regular exercise (AOR = 3.01, P = 0.003), a perceived health status of fair or bad (AOR = 4.34, P < 0.001), and somatic climacteric symptoms (AOR = 2.32, P = 0.012) were independent and significant factors of depressive symptoms in late middle-aged Taiwanese women. CONCLUSIONS: Findings from this secondary analysis of a population-based survey suggested independent associations of somatic climacteric symptoms, and a number of socio-demographic and health-related factors with depressive symptoms in late middle-aged Taiwanese 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".