Youth Health Risk Behavior Assessment among University Students in Baghdad, Iraq
Bibliographic record
Abstract
INTRODUCTION: Health risk behavior is one of the leading causes of morbidity and mortality in the world. The social and economic costs associated with these behaviors can be reduced by changes in individuals’ behavior. Health-related behavior can either enhance or damage physical, psychological and social wellbeing. Human behavior is influenced by an individual’s health consciousness. OBJECTIVE is to study health related behavior that includes dietary habits, physical activity, smoking and alcohol consumption, screen time and behavior related to unintentional injury among students attending public universities in Baghdad. METHODS: Study design is Cross Sectional Study; sampling method is multi-stage Cluster sampling. Self-administered questionnaire was used to collect student data, a total of 1836 students participated in the study RESULTS: Among the 1836 students who participated in the study, 21.4% are smokers, only 3.4% use seatbelts while driving, 11% took medicine without prescription and 78% did not perform regular exercise. 85% of students use the internet at least three hours per day. Dietary habits of students were average based on diet recommendation. CONCLUSION: Health related behavior traits were average among university students in Baghdad. Policy changes to reinforce stringent road traffic safety measures and initiate regular health promotion programs in universities to motivate students to be more proactive towards their health and fitness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".