A study to analyze anxiety disorder of a group of engineering students during the second wave of COVID-19
Bibliographic record
Abstract
BACKGROUND: The second wave of COVID-19 has profoundly affected every sector of Indian, especially the student community. Hence, this study started with two objectives. The first is to analyze the anxiety level, among the students of an engineering college in India, during this pandemic. The second objective is to find an accurate prediction model to apprehend the level of anxiety beforehand. It will enable us to cater to timely crisis-oriented psychological services and take precautionary actions to reduce the anxiety level of this community in future crisis periods.METHODS: To assess the psychological state of the students, the General Anxiety Disorder Questionnaire (GAD-7) has been employed. An online survey has been conducted in the month of April-May 2021 where 756 students of the Hooghly Engineering and Technology College (HETC) of West Bengal, India, had participated. To build an accurate predictive model through comparative analysis, four well-known machine-learning algorithms have been employed.RESULTS: The study shows that 11.24% of participants have severe anxiety levels, 13.49% of participants have moderate, 24.73% have mild and 50.52 participants have no anxiety at all. It also shows the students of 4th years have higher scores of anxiety (OR=1.22, 95%, CI =0.89-1.65) and 1st-year students having lower scores in all measures compared to their senior, while the Female students having more psychological impact, compared to Male students. The proposed predictive model has achieved 99% accuracy.CONCLUSIONS: The study shows the presence of a higher degree of anxiety among students compared to previous studies conducted by other researchers during the first wave as mentioned in the survey.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".