Eating pattern among adolescent female student, Applied Medical Sciences College, University of Hafr-Al Batin
Bibliographic record
Abstract
The college students, representing the young age population of community, for different reasons are prone to eat unhealthy foods and to have bad health habits during their college years which might affect their well-being and increase the risk of obesity, diabetes, and coronary heart disease; like fast food consumption, lower vegetable and fruit intake in face of less physical activities and a lot of computer & TV watching hours. This study aimed to assess eating habits and patterns, factors affecting food choices and anthropometric measurements. Descriptive cross-sectional study method was followed. 230 students were included in the study. The findings revealed that 50.9% of the study sample were at age group (< 20 years), nearly half 48.7% were at a preparatory year. Results show also that 44.3% of the study sample don't take breakfast regularly; the most reported causes were not enough time at home 49% and that they don't prefer cafeteria food 24.6% nor there is no for a break in the timetable 21.6%. The results show that 53.04% had a normal BMI and 24.35% were overweight. The BMI had a significant relation with the consumption and snacking patterns among students (p = .000). So, there is a greater need for constructing educational programs to be directed to enhance the nutritional status of the university adolescent students.
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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 | 0.000 |
| 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".