Prevalence and factors associated with suicidal ideation among students taking university entrance tests: revisited and a study based on Geographic Information System data
Bibliographic record
Abstract
BACKGROUND: A previous study identified students taking Bangladeshi university entrance tests as a vulnerable group at a higher risk of suicidal behaviours before the COVID-19 pandemic. However, the impact of the pandemic on the magnitude of these behaviours among this population remains unexplored. AIMS: This study aimed to investigate the prevalence of suicidal ideation and associated factors among Bangladeshi university entrance test takers following the pandemic. In addition, an approach based on Geographic Information System (GIS) data was used to visualise the distribution of suicidal ideation across the country. METHODS: A cross-sectional approach was used to collect data among participants taking the entrance test at Jahangirnagar University in September 2022. Using SPSS, data were analysed with chi-squared tests and binary regression, and ArcGIS was used to map the results across the nation. RESULTS: The study revealed a prevalence of 14.4% for past-year suicidal ideation, with 7.4% and 7.2% reporting suicide plans and attempts, respectively. Notably, repeat test-takers exhibited a higher prevalence of suicidal behaviours. Significant risk factors for suicidal ideation included urban residence, smoking, drug use, COVID-19 infection and deaths among close relations, depression, anxiety and burnout. The GIS-based distribution indicated significant variation in the prevalence of suicidal ideation across different districts, with higher rates observed in economically and infrastructurally deprived areas. CONCLUSIONS: Urgent measures are needed to address the high prevalence of suicidal behaviours among students taking university entrance tests students in Bangladesh, particularly in light of the COVID-19 pandemic. Enhanced mental health support, targeted prevention efforts and improved resources in economically disadvantaged regions are crucial to safeguard the well-being of these students.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".