Weight-loss strategies of South African female university students and comparison of weight management-related characteristics between dieters and non-dieters
Bibliographic record
Abstract
BACKGROUND: Female university students are at risk for weight gain and use of inappropriate weight-loss strategies. By gaining a greater understanding of the weight-loss strategies used by and weight management related characteristics of these students, effective weight management interventions for this vulnerable group can be developed. METHODS: Two hundred and fifty female students from South Africa universities, aged 18-25 years, participated in this cross-sectional study; 162 attempted weight loss during the year preceding the study (dieters) and 88 were non-dieters. Weight and height were measured and BMI (kg/m(2)) computed. A self-administered questionnaire was used to record all other variables. Weight loss strategies were described for dieters and compared between BMI groups within the dieters group. Weight management related characteristics were compared between dieters and non-dieters. Statistical tests included Pearson Chi-square test, independent samples t-test or Mann-Whitney U test (depending on distribution of the data). Predictors for a higher BMI and being overweight/obese (BMI ≥25 kg/m(2)) were identified using regression models. RESULTS: Healthy weight-loss strategies included increased exercise and fruit/vegetable intake and decreased intake of sugar and fat containing items; unhealthy methods included eating little food and skipping meals; and extreme weight loss strategies included laxatives and vomiting. The most commonly used weight-loss product was Herbex. Dieters were characterized by a higher BMI, overestimation of their weight (especially normal weight students), dissatisfaction with weight and select body parts, higher intake of breakfast and healthy foods, lower intake of unhealthy foods, higher levels of vigorous physical activity, higher use of select informal weight-loss information sources and experiencing more pressure to lose weight from mothers, siblings and friends. Predictors of higher BMI and/or increased risk for BMI ≥25 included weight-loss attempt during the past year, race, dissatisfaction with waist, perception of currently being "chubby," and higher frequencies of intake of a snack and fatty foods. CONCLUSION: Attempting weight-loss is common among female students and predicts BMI. Healthy (mainly), unhealthy and extreme weight loss methods are used. Dieters are characterized by a less realistic body image, lower body satisfaction, higher pressure to lose weight, use of informal weight-management information and a healthier life-style.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".