Technology anxiety (technostress) and academic burnout from online classes in university students
Bibliographic record
Abstract
Pandemic moments have generated mental and emotional problems in students at all levels. These have been affected by the format of virtual classes, the mandatory confinement and the little physical relationship due to the existing restrictions, generating academic burnout and anxiety in university students. In this context, the objective was to know the existing relationship between burnout and anxiety in students of the FIS-UNCP, the 15-question Maslach Burnout Inventory Student Questionnaire (MBI-SS) was used with the dimensions: Emotional Exhaustion, Cynicism and Loss of Academic Efficacy and 5 questions to know the level of technological anxiety or technostress, with a population of 328 university students of 10 semesters, through the questionnaire in Office Forms. The research design was non-experimental, transectional, with a qualitative-quantitative approach and descriptive-explanatory levels. The descriptive data analysis was made based on the scale, allowing the identification of students with burnout and the structural equation modeling facilitated the establishment of the relationship between the variables. The study showed that 26 students (7.93%) suffer from academic burnout. At the same time, it has been demonstrated that there is a positive and significant relationship between emotional exhaustion and lack of academic efficacy, with technological anxiety with path values of 0.701 and 0.345 respectively, the p-values allowed demonstrating hypotheses 1 and 3 formulated. At the level of the structural model, it allows anticipating future results, since the coefficient of determination (R2) calculated was 0.838.
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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 | 0.001 | 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) | 0.002 | 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".