Suicidal ideation among people with disabilities during the COVID-19 pandemic in Bangladesh: prevalence and associated factors
Bibliographic record
Abstract
BACKGROUND: Evidence from pandemic and pre-pandemic studies conducted globally indicates that people with disabilities (PWDs) have a higher risk for suicidality. However, none of these studies has assessed suicidality among PWDs in Bangladesh. AIMS: The purpose of this study was to determine the prevalence of and factors associated with suicidal ideation among PWDs during the COVID-19 pandemic in Bangladesh. METHOD: Using a snowball sampling technique, a cross-sectional survey was conducted from February to April 2021 among PWDs from six districts in the northern region of Bangladesh. Information related to sociodemographic factors, clinical characteristics, behavioural factors and suicidal ideation was collected. Chi-squared test and logistic regression were used to describe the data and explain the relationship of factors associated with suicidal ideation. RESULTS: The prevalence of COVID-19-related past-year suicidal ideation was 23.9%. The factors associated with suicidal ideation included: age above 35 years, being female, acquiring a disability later in life, lack of sleep and current substance use. In addition, higher education appeared to be a protective factor against suicidal ideation. CONCLUSIONS: This study highlighted that PWDs had an increased risk of suicide; that is, one-fourth of them had past-year suicidal ideation. This may have been because of COVID-19-related restrictions and stressors. Thus, the government and policy makers need to pay more attention to developing effective suicide assessment, treatment and management strategies, especially for at-risk groups, to minimise the impact of the COVID-19 outbreak.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".