Análisis de la adicción al smartphone en estudiantes universitarios
Bibliographic record
Abstract
Los smartphones son el dispositivo móvil más utilizado por parte de la población. Su uso se ha intensificado en los últimos años, dando lugar a comportamientos adictivos entre los más jóvenes. Los objetivos de este trabajo fueron evaluar el grado de adicción al smartphone, determinar los factores sociodemográficos que influyen en la adicción y en la autoestima y establecer la correlación entre estas dos variables. La muestra se compuso por estudiantes universitarios de la Facultad de Ciencias de la Educación de la Universidad de Granada (N = 385), con edades comprendidas entre los 18 y 46 años (M = 24,08; DT = 5,00). Se utilizó una metodología cuantitativa aplicando dos escalas estandarizadas internacionalmente. Los hallazgos del estudio confirmaron un grado medio-alto en la adicción al smartphone, la influencia del tiempo de uso en la adicción al smartphone y la edad y la titulación académica como factores influyentes en la autoestima. Por otro lado, el modelo de ecuación estructural mostró una correlación negativa entre la adicción al smartphone y la autoestima. Finalmente, los datos obtenidos alertan del mal uso que los estudiantes universitarios le están dando a los dispositivos móviles y de la necesidad de implementar medidas para evitar comportamientos adictivos. Smartphones are the mobile device most used by the population. Its use has intensified in recent years, leading to addictive behaviours among younger people. The objectives of this paper were to evaluate the degree of smartphone addiction, determine the sociodemographic factors that influence addiction and self-esteem, and establish the correlation between these two variables. The sample was made up of university students from the Faculty of Education Sciences of the University of Granada (N = 385), aged between 18 and 46 (M = 24.08; DT = 5.00). A quantitative methodology was used applying two internationally standardized scales. The study findings confirmed a medium-high degree in smartphone addiction, the influence of usage time on smartphone addiction and age and academic degree as factors influencing self-esteem. On the other hand, the structural equation model showed a negative correlation between smartphone addiction and self-esteem. Finally, the data obtained warns of the misuse that university students are giving to mobile devices and the need to implement measures to prevent addictive behaviour.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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; 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".