Anthropometric Characteristics and other Dietary Aspects of a Group of Spanish Women Looking for Weight Loss and Enrolled in a Weight Management Program
Bibliographic record
Abstract
Overweight is a health problem characterised as a higher than normal body weight due to an abnormal increase in body fat. Body weight adequacy is categorised using body mass index (BMI), however other parameters as fat mass (FM), waist circumference or waist to hip ratio, are relevant. Ideally, body composition should be calculated initially to evaluate changes during a dietary intervention for weight loss. Hunger experience is another parameter to take into account in order to succeed. The aim was to investigate and describe the characteristics of women seeking weight loss solutions. We organised an open program for people with body excess who wanted to lose weight. 252 women participated and answered to a dietary interview. Anthropometric measures of weight, height, body mass index, body fat, waist and hip circumference were taken. The mean age was of 36.84±7.29 years, and most of them, about 90%, have followed dietary programs for weight loss throughout their lives. They all wanted to lose weight in a range of 3 to 20 kilograms with a mean value of 11.49±6.01 kilograms. 123 women had a hunger profile of satiating behaviour and 129 a snacking one. The mean BMI was within overweight values, and mean fat mass was within obesity values. Waist and hip circumference were higher than normal in most of the participants and excess body weight perception and attitude were different. There is a need to tackle overweight and obesity individually, taking into account personal consciousness and expectancy, anthropometric measures and hunger experience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".