University Students' Viewpoints: A Coping Mechanism amid the Covid-19 Pandemic
Bibliographic record
Abstract
The crisis we encounter in the global community is paramount to all species of social interaction. COVID-19, previously known as 2019 nCoV has devastated our day-to-day lives from our financial capability to our emotional condition. According to Rubin and Wessely (2020), the widespread contagion will inevitably have a psychological effect. This study aims to explore the different coping mechanisms among university students with the current global crisis, determine the significant difference of coping among gender preferences, and identify to what extent university students have been able to cope. Data was collected through a researcher-made survey questionnaire and an instrument adapted from Carver (1997). The survey was administered to university students. Students who responded and gave their consent were included in the study. Based on the results, the top five coping strategies that the students use as per experience are “listening to music”, “sleeping”, “social media”, “movie/Netflix”, and “online games”. However, it is also notable that none of the students believed that using “prohibited drugs” or “substance use” is an option in coping with this pandemic. Moreover, there is no significant difference in coping among gender preferences which implies that regardless of your gender preference, orientation, and identity, all want to deal with their problems, hardships, or stresses in life. Hence, diverting one’s attention to other things somehow is the students’ best way of coping, armoured with positivity and faith.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".