A Comprehensive Review of Psychosocial, Academic, and Psychological Issues Faced by University Students in India
Bibliographic record
Abstract
Background: University students confront a wide range of issues during their pursuit of education. Understanding these issues is essential for developing effective treatments and support systems. Purpose: This study aims to delineate the landscape of scholarly literature pertaining to psychosocial, academic, and psychological issues among university students. It further identifies key journals and publishing trends within the fields, thereby significantly contributing to this domain. Additionally, this study outlines the scientific field networks that offer theoretical and conceptual foundations for exploring the psychosocial, academic, and psychological challenges faced by university students. Furthermore, it also intends to systematically categorise various types of problems encountered by university students in India. Methods: To systematically gather and investigate the problems encountered by students in higher education, this study utilises bibliometric analysis, highlighting topics related to mental health. Data were extracted from Scopus and Web of Sciences databases. Results: The analysis of the literature yielded 12 overarching categories related to challenges faced by university students: stress, academic stress, depression, anxiety, internet/ smartphone addiction/ gaming disorder, low self-esteem, loneliness, insomnia, suicidal ideations, eating disorders, drug addiction, adjustment issues. Conclusion: Academic institutions should prioritise student mental health, as it affects academic performance and can lead to psychological disorders. Universities need Guidance and Counselling Cells staffed with professionals to help students manage psychosocial, academic, and psychological challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".