Tecnoestrés en estudiantes universitarios. Diagnóstico en el marco del covid-19 en México
Bibliographic record
Abstract
Objective: Identify technological stress or techno stress in students of Higher Education Institutions in Mexico, in the first quarter of the year of the COVID-19 pandemic . Method: It is a quantitative study with a descriptive scope. 517 surveys were applied to measure four dimensions of technostress . Results: According to the dimensions considered, the following scores were obtained on a scale from 1 to 5: a. Attitudes towards information and communication technologies (ICTs): 2.9; b. School stress: 3.2; c. Effects on the use of ICTs: 2.1; and d. Social Networks and ICTs in Education: 3.87. It was also found that students spend almost 8 hours a day using ICTs for academic activities, which exceeds the usual time. Discussion y Conclusion: Due to the pandemic, the sudden change from face-to-face education to distance and online education using various ICTs, has generated effects in students such as anxiety, depression and stress, which is why intervention to reduce said effects is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".