Key Findings from Mental Health Research During the Menopause Transition for Racially and Ethnically Minoritized Women Living in the United States: A Scoping Review
Bibliographic record
Abstract
Background: Racially and ethnically minoritized (REM) women experience social and structural factors that may affect their response to mental health treatment and menopausal symptoms during the menopause transition (MT). This scoping review on mental health during the MT for REM women in the United States was conducted to characterize factors associated with mental health challenges. Materials and Methods: Five databases were searched. Articles were included if focused on MT in REM women in the United States and its territories with specific mental illnesses and published in English from 2005 to 2021. Titles and abstracts and full text were screened. Screening and data collection were completed in duplicate by two reviewers in Covidence. Results: Sixty-five articles were included and indicate that REM women experience a disproportionate burden of depressive symptoms during the MT. Less evidence is reported about anxiety, Post-Traumatic Stress Disorder, psychosis, schizophrenia, and other mental illnesses. The risk factors associated with mental illness during MT are social, structural, and biological. Treatment response to therapeutic interventions is often underpowered to explain REM differences. Conclusion: Depression during the MT is associated with negative outcomes that may impact REM women differentially. Incorporating theoretical frameworks ( e.g. , intersectionality, weathering) into mental health research will reduce the likelihood that scientists mislabel race as the cause of these inequities, when racism and intersecting systems of oppression are the root causes of differential expression of mental illness among REM women during the MT. There is a need for interdisciplinary research to advance the mental health of REM 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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".