Saudi Arabian Dental Students’ Knowledge and Beliefs Regarding Obesity in Children and Adults
Bibliographic record
Abstract
The objectives of this study were to determine knowledge/beliefs of a group of Saudi Arabian dental students regarding overweight/obesity (OW/OB). Dental students (fourth-year, fifth-year, and interns) at King Saud University College of Dentistry completed an anonymous questionnaire regarding OW/OB in children and adults. Frequency distribution and chi-square analyses were done. The total respondents were 260 (response rate=87 percent), most of whom were male (59 percent). Half of the respondents reported their knowledge of OW/OB in adults/children to be average, with knowledge of pediatric OW/OB rated lower (37 percent reported it as fair/poor) than adult OW/OB (17 percent reported it as fair/poor). Only a third (34 percent) of the respondents selected body mass index (BMI) as the best method to identify OW/OB. More than half of the respondents correctly believed that OW/OB was a problem in many adults/children in Saudi Arabia. A slightly higher proportion endorsed a role for dentists in the identification/prevention of OW/OB in children (76 percent) as compared to adults (69 percent). Female respondents had better knowledge than males about OW/OB and were more likely to correctly select BMI as the best method for identifying OW/OB. These findings may provide support for the expansion of education in these areas in the dental curriculum.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".