Depression, hormone therapy, and the menopausal transition among women aged 45 to 64 years using Canadian Longitudinal Study on aging baseline data
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between menopausal status, hormone therapy (HT) use and the presence of depressive symptoms among middle-aged women in Canada. METHODS: Cross-sectional baseline data from 13,216 women aged 45 to 64 years from the Canadian Longitudinal Study on Aging (CLSA) was used. The association between menopausal status (pre- vs postmenopausal) and self-reported symptoms of depression based on a score of 10 or more on the Center for Epidemiologic Studies Short Depression Scale-10 was assessed using logistic regression. Use and duration of use of HT, time since menopause, age at onset of menopause, and socioeconomic status and other contextual variables were explored for the association with depression. RESULTS: Overall, 18.4% of middle-aged women in the CLSA data were identified as depressed using the Center for Epidemiologic Studies Short Depression Scale-10. Based on the logistic regression models, women reporting premature menopause (before the age of 40 years) and postmenopausal women currently using HT had 1.45 (1.07-1.97) and 1.21 (1.02-1.44) greater odds of having depression. Chi-square analyses showed that women with depressive symptoms were more likely to have low education, low household incomes, live alone, be nulliparous, and have low social support. CONCLUSIONS: Our findings highlight the association between depression and premature menopause among midlife women. Current HT use may be a proxy for more severe menopausal vasomotor symptoms, a known risk factor for depressive symptoms. Identification of risk factors, including social determinants of health, age at menopause, and menopausal symptoms can help guide clinicians when assessing mental health. : Video Summary:http://links.lww.com/MENO/A576.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".