Evaluation of nutritional status and eating habits of Polish women during the menopause transition – a pilot study
Bibliographic record
Abstract
Introduction:The menopause is a natural biological process in a woman's life when the hormonal activity of the ovaries ceases.It has an impact on the woman's further life.In this study we analysed nutritional status and nutrition of women during the menopause transition. Material and methods:The study was carried out in 2018 using an online questionnaire survey.Data were obtained from 90 women from Poland aged between 40 and 55 years.The subjects were selected using the snowball method.The questionnaire included questions about demographic and social situa tion, lifestyle, and health as well as questions related to the menopausal period, diet, and eating habits of the respondents.The Food Frequency Questionnaire (FFQ) was used to obtain data related to the diet.The results were analysed using STATISTICA version 13 (StatSoft, Inc.).Results: Almost half of the respondents were overweight and over 20% were obese.Abdominal obesity was found in 71% of the women.Older age was significantly associated with higher waist circumference.Most women did not follow any special diet.The most consumed products during the day were fruit and vegetables but also wholegrain products, dairy products, meat, and cereal products.Women who often consumed whole grains, buckwheat, fatty fish, legumes, soy products, and green leafy vegetables more frequently reported a significantly lower intensity of vasomotor symptoms, fatigue, and insomnia.Conclusions: The study suggests that the excessive body weight among women during the menopausal transition is a common problem.Women should be encouraged to maintain a healthy lifestyle during the lifespan, also because a proper diet improves the quality of life associated with the symptoms of meno pause.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".