8000003 Are health-related, lifestyle, work-related, and socio-demographic factors associated with work productivity among menopausal women? a systematic review
Bibliographic record
Abstract
<h3>Objective</h3> An increasing number of women at menopausal age, of whom many experience menopausal symptoms, are participating in the workforce. Understanding the factors that influence work productivity in this life stage can inform the development of targeted interventions. The goal of this systematic review was to explore which health-related, lifestyle, work-related, and socio-demographic factors are associated with work productivity among menopausal women. <h3>Methods</h3> A systematic search was conducted for observational studies in PubMed, PsycINFO, and Embase up to July 2024. The risk of bias was assessed using an adapted Newcastle-Ottawa scale. The GRADE framework for prognostic research was applied to evaluate the quality of evidence. <h3>Results</h3> A total of 29 studies were included. Menopausal symptoms in general, as well as psychological and vasomotor symptoms, and lower sleep quality were associated with lower at-work productivity, with moderate to high-quality of evidence. Additionally, there was moderate-quality of evidence that better (perceived) health was associated with higher at-work productivity. Regarding absenteeism, moderate evidence was found for an association with vasomotor symptoms. Inconclusive evidence was found for socio-demographic, work-related factors and remaining health-related and lifestyle factors in relation to both at-work productivity and absenteeism. <h3>Conclusion</h3> This review highlights the association of menopausal symptoms and poor sleep quality with decreased work productivity in menopausal women. The evidence for other associations was limited due to the low quality of available evidence or the lack of sufficient studies. Further research on modifiable lifestyle and work-related factors, is needed to improve the work functioning of women during menopause.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".