Asociación entre la adicción a la tecnología y el rendimiento académico en universitarios latinoamericanos en 2024
Bibliographic record
Abstract
Introduction: The use of new technology devices connected to the Internet is daily access for most university students, who may become addicted to it with a negative impact on their academic performance. Objective: To determine the relationship between addiction to technological equipment and poor academic performance. Methodology: An observational and correlational design was applied. The study population was made up of Latin American university students in 2024. Technology addiction was measured with the Labrador JF questionnaire and academic performance with a perception test. A telematic questionnaire distributed through social networks was applied. Descriptive and analytical statistics were applied with the Epi Info 7™ program. Results: The sample included 445 students, 320 women (71.91%) with a mean age of 23 ± 9 years and 125 men (28.09%) with a mean age of 22 ± 7 years. Most reside in Peru and Paraguay. The most used devices were cell phones and computers (46.74%). 68.99% (n 307) do not have their own economic income. The average time spent daily using these electronic devices was 4 hours. Addiction to technological equipment was detected in 165 (37.08%) students and low academic performance was reported by 281 students (63.15%). Analyzing both variables, a statistically significant association was found: RR 1.21 (95% CI 1.05 – 1.39) (p 0.01). Conclusions: A statistically significant association was found between dependence on technological equipment and low academic performance in university students. Timely detection of this habit and the application of therapeutic strategies to mitigate the excessive use of electronic equipment are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".