Psychological disorders among college going students: A post Covid-19 insight from Bangladesh
Bibliographic record
Abstract
The COVID-19 pandemic has been found to result in adverse effects on both the physical and mental well-being of individuals. The adolescent population emerged as one of the most susceptible cohorts affected by the ongoing pandemic. They experienced significant adversity due to various mental health conditions. The objective of this study was to evaluate the present prevalence rates of depression, anxiety, and internet addiction among college-going students in Bangladesh following the post-COVID period. The study involved a cohort of 7667 students. A cross-sectional study was conducted to evaluate the levels of depression, anxiety, and internet addiction among college-going adolescents. The assessment utilized the Patient Health Questionnaire (PHQ-9), Generalised Anxiety Disorder (GAD-7), and Young's Internet Addiction Test (IAT) scales. The data was analyzed using the Pearson chi-square test and binary logistic regression. Participants averaged 15.3 years old and 64.3% female. 63% of students fulfilled the criterion for internet addiction, 37% did not, 75% met depression criteria, 25% did not, and 60% met anxiety requirements. Girls were more depressed and anxious than boys. Boys were more internet-addicted than girls. Social media usage from COVID-19, daily exercise, online courses, and financial concerns throughout the pandemic affected participants' mental health. Still, the students were suffering from internet addiction, depression, and anxiety after COVID-19. Early identification and intervention may lessen these difficulties' influence on adolescents' academic and personal lives. Colleges may provide mental health services, encourage healthy lives, and educate on mental health.
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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 | 0.000 | 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) | 0.002 | 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".