An Exploration of the Mental Health impact among Menopausal Women: The MARIE Project Protocol (International Arm)
Bibliographic record
Abstract
ABSTRACT Introduction Menopause is characterised by the ending of the menstrual cycle as part of a natural process. However, menopause can also be caused by other health conditions, such as premature ovarian failure or cancers that may have led to an oophorectomy or a radical hysterectomy. The physiological and psychological mechanisms linked to menopause across all age groups, races and ethnicities are not well understood. The paucity of data could reduce the advancement of optimal clinical practice, leading to reduced quality of life for women. To better explore and assess menopause, we have designed the MenopAuse mental hEalth Rating (MARiE) tool. Methods We will conduct a prospective mixed methods study using two workstreams of WP2a and WP2b among in women and trans-men ≥18 years old that are experiencing perimenopause, menopause or post-menopause among an array of ethnicities and races. WP2a will involve a number of validated clinical assessments of Hospital Anxiety and Depression Scale, Insomnia Severity Index Scale, Menopause Rating Scale, Greene Climacteric Scale, Health Related Quality of Life, Quebec Pain Disability Scale, and Burnout Assessment Tool will be administered digitally using the Qualtrex platform. WP2b will assess the feasibility of using a novel tool called MARiE to report face validity and efficacy. Ethics approval Research Ethics approval reference for this study in the UK is 22/EE/0159. As required, country-specific approvals have been obtained and continue to be secured. Dissemination The study findings will be made available using a peer-review publication journal, workshops and conferences.
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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 | 0.052 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".