Mental Health Problems among College Students in India during the COVID-19 Pandemic in the Context of Disruptions in Academics and Interpersonal Relationships
Bibliographic record
Abstract
The COVID-19 pandemic has led to significant disruptions in daily lives, contributing to mental health problems around the world, with young adults being a particularly vulnerable population for mental health problems (Varma et al., 2021). In the current study, we explored perspectives on how the pandemic had affected their lives, and examined frequency of mental health problems among college students in India during the middle phase of the pandemic. Participants (N = 455, 65% women, Mage = 20.62 years) responded to open-ended questions and completed self-report measures of anxiety, depressive symptoms, emotion dysregulation, and dysfunctional coping, and skills use. Thematic analysis of open-ended responses yielded nine themes across three domains: Major concerns, impact on academics and learning, and impact on relationships. Mental health symptomatology was identified as the most common concern, and approximately 50% of the sample scored above the clinical cut off on the self-report measure for either anxiety or depression, indicating a moderately high level of distress. Difficulties in effectively regulating one’s negative emotions and dysfunctional coping uniquely predicted higher anxiety and depression, whereas adaptive coping predicted lower depression. The findings demonstrate that college students in India are struggling with mental health during the pandemic. Facilitating emotion regulation and coping may be potential targets for intervention.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".