Knowledge, Attitudes and Practices toward Energy Drinks among Adolescents in Saudi Arabia
Bibliographic record
Abstract
The objective of this study is to explore the knowledge, attitudes and intake of energy drinks among adolescents in Saudi Arabia. A multi-stage stratified sampling procedure was carried out to select 1061 school children aged 12-19 years, from Jeddah city, Saudi Arabia. A short self-reported questionnaire was administrated in order to collect the data. Of adolescents in the study, 45% drank energy drinks (71.3% males and 35.9% females; P<0.001). Advertisements were the main source of information on energy drinks (43%). The major reasons for consuming energy drinks were taste and flavour (58%), to 'try them' (51.9%) and 'to get energy' (43%), albeit with significant differences between genders (P<0.001). About half of the adolescents did not know the ingredients of these drinks, and 49% did not know that they contain caffeine (P-values <0.006 and <0.001 between genders, respectively). The greater majority (67%) considered energy drinks to be soft drinks. The study indicates the need for Saudi adolescents to be warned on the over-consumption of energy drinks. The study brings to attention the need for educational programmes related to increasing awareness in the community of the health effects related to high consumption of energy drinks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".