Prevalence and factors associated with depression, anxiety, and stress among people with disabilities during COVID-19 pandemic in Bangladesh: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a profound impact on the mental health of individuals across various populations. People with disabilities (PWDs) are particularly vulnerable to these effects, yet there is a lack of studies investigating the mental health of PWDs in Bangladesh. This study aims to investigate the prevalence of and factors associated with depression, anxiety, and stress among PWDs during the COVID-19 pandemic in Bangladesh. METHODS: Data was collected through interviews with 391 PWDs between December 2020 and February 2021. Demographic information, clinical characteristics, and scores from the Depression, Anxiety, and Stress Scale (DASS-21) were obtained. Chi-square tests and logistic regression analyses were conducted to examine the relationship between psychological measures and potential risk factors. RESULTS: The prevalence was found to be 65.7% for depression, 78.5% for anxiety, and 61.4% for stress, respectively. Several factors were identified as associated with these mental health issues, including gender (male), marital status (being married), low education levels, multiple impairments, comorbid medical illnesses, poor sleep quality, rural residency, hearing disability, disability onset later in life, and testing positive for COVID-19. CONCLUSIONS: The prevalence was found to be 65.7% for depression, 78.5% for anxiety, and 61.4% for stress, respectively. Several factors were identified as associated with these mental health issues, including gender (male), marital status (being married), low education levels, multiple impairments, comorbid medical illnesses, poor sleep quality, rural residency, hearing disability, disability onset later in life, and testing positive for COVID-19.
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 | 0.000 | 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".