University Students’ Response to Supporting Mental Health During Covid-19 Pandemic from Positive Psychology Approach: A Literature Review
Bibliographic record
Abstract
The outbreak of COVID-19 pandemic is prevailing across many countries where millions of people are affected. In response to the current situation, many countries launch prevention measures, such as quarantine or social distancing, that aim to curb the COVID-19 spread. However, physical isolation can lead to serious mental health problems for many people, especially children who are still learning how to recognize and control emotions. Therefore, it is vital to provide productive psychological interventions for these individuals who are in demand of psychological aids. After searching and analysing past papers, positive psychology, a perspective emerging from 21st century, is worth academic attention when tackling mental health problems induced by acute stress. This literature review will thus mainly talk about the theory of positive psychology along with its application in clinical fields and suggest that it can be adopted as a promising intervention for helping university students with respect of improving well-being in the face of COVID-19 pandemic outbreak.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".