Walking together: women with the severe symptoms of menopause propose a platform for a walking program; outcome from focus groups
Bibliographic record
Abstract
BACKGROUND: Menopause and midlife are stages in a woman's life that can be marked by debilitating symptoms and increasing risks for cancer, cardiovascular, metabolic, and bone health issues. Walking represents a simple, low cost, and widely accessible activity with proven health benefits, though its therapeutic effect on alleviating menopause symptoms is not well characterized. Women are generally not opposed to exercise programs; however, increasing or maintaining exercise levels remains a challenge. We undertook a qualitative descriptive study to explore features of a walking program that would be conductive to menopausal women's participation, as well as to inform the development of such a program. METHODS: We conducted focus groups with women recruited from two menopause clinics and who suffered from moderate to severe menopause symptoms. The focus groups were audio recorded and transcribed. Women were prompted to talk about their menopause experience and exercise practice and how they would envision a walking exercise program that would keep them engaged. Qualitative content analysis was used to analyze the data and to identify characteristics of a walking exercise program. RESULTS: Twenty women participated in 5 focus groups. Women were very interested in trying walking as a means of staying healthy and possibly reducing menopause symptoms. Four major characteristics emerged as important for a walking program: (a) sensitivity to health realities of menopausal women, (b) inclusivity of various needs/levels of physical ability, (c) attentiveness to the need for mutual social support, (d) flexibility in planning of locations and scheduling. A restricted social network platform with features catering to women in menopause was suggested as suitable to initiate and sustain an adequate walking program. CONCLUSIONS: The findings of this study will be essential in designing a program that would be attractive for women to start and maintain a walking habit. The program would assist in elucidating whether walking is a useful and valuable alternative therapy for menopausal symptoms and, ultimately, might help women staying fit in midlife and postmenopausal.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".