<i>Dietary Intakes, Attitudes Toward Carbohydrates of</i> Postmenopausal Women Following Low Carbohydrate Diets
Bibliographic record
Abstract
PURPOSE: Middle-aged women have the highest levels of obesity and comprise the largest group of dieters. Few investigators have examined how women apply weight-loss diet principles in an unsupervised setting. Dietary intakes and attitudes toward carbohydrates were examined in women who were self-reported low carbohydrate dieters (SRLCDs); these intakes and attitudes were compared with those of women who were following their normal diet (non-dieters [NDs]). METHODS: A convenience sample of 29 postmenopausal women aged 45 to 65 was recruited. Data were obtained by interview, questionnaire, and direct anthropometric measurement. Descriptive statistics, chi-square analysis, and analysis of variance were used to compare groups. RESULTS: Although total energy and protein intakes were similar, SRLCDs consumed significantly more fat and less carbohydrate (expressed as a percentage of total energy) and more cholesterol and less fibre than did NDs. Both groups had unfavourable attitudes toward carbohydrates. CONCLUSIONS: The SRLCDs ate more fat than recommended. Women who are considering following a low carbohydrate diet need to know the nutritional risks of unbalanced self-designed low carbohydrate diets. Negative attitudes toward carbohydrates were not confined to dieters. Nutrition education is necessary to help consumers understand basic nutrition principles and to be more skeptical of fad diets.
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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 | 0.000 | 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".