Frecuencia y Razones de Consumo de Bebidas Energéticas en Jóvenes Universitarios
Bibliographic record
Abstract
Introduction: Energy drinks are products composed of various substances that could cause harmful health effects. The consumption of these drinks has increased in recent years in young people seeking effects that help improve their performance in academics or sports. Objective: To determine the frequency and principal reasons for the consumption of energy drinks, as well as the effects associated with their consumption, in students from the University of Canada. Methodology: A qualitative, descriptive, observational and crosssectional study was conducted. Surveys with a confidence level of 95% and a precision of 5% were applied. From the total of 334 students enrolled in different careers at the University, 179 students were surveyed using simple random sampling. Results: 68.5% of respondents mentioned that at least once in their life they have tried energy drinks, while 31.28% consumed it during the investigation. 76.78% reported they consume on average 1 to 3 cans a month. The main reasons cited for consumption were: to stay awake, increase sports performance and enhance the effects of alcohol. The most preferred brands were Vive 100 followed by Red Bull. For both preferred brands, palpitations and headaches are the main adverse effects. Conclusions: Although the consumption of these beverages is low, it is necessary to implement a dissemination program to raise awareness among students about the adverse effects associated with the consumption of energy drinks.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".