Knowledge, Attitudes, and Practices Associated with COVID-19 Among Rickshaw Pullers of Dhaka, Bangladesh
Bibliographic record
Abstract
Background: COVID-19 pandemic has brought a worldwide disaster, and Bangladesh also passes through so many challenges to face this unexpected suddenly arising health problem. On the other hand, rickshaw is the most common and a very convenient way of transport in Dhaka city. Dhaka’s 2 million rickshaw pullers are vital to urban transport, but they are socioeconomically vulnerable. We assessed the knowledge, attitude, and practice (KAP) of rickshaw pullers of Dhaka city regarding COVID-19. Methods: This descriptive cross-sectional study was conducted from 1st to 10th April 2021 among rickshaw pullers who came to Northern International Medical College and Hospital (NIMCH) with patients or passengers. Systematic random sampling was approached every 5th rickshaw puller during morning (06:00-09:00) and evening (17:00-20:00) peaks to select the study population, and the final sample size was 172. The knowledge, attitude, and practice (KAP) of rickshaw pullers regarding COVID-19 were assessed using 10, 5 and 4 questions respectively with a score of 1 for each correct or positive response and 0 for each incorrect or negative response. A score of ≥ 50% was considered good and a score of < 50% was considered poor. The data were analysed via SPSS (version 22.0). Results: In this study, the majority of the participants were 31-50 years of age (48.8%), married (79.1%), living with family members (58.7%) and Muslim (97.1%), while two-fifths (43.6%) could only sign their names. Almost all the participants (95.9%) learned about COVID-19 through television. Rickshaw pullers have poor level of knowledge (47.6% good & 52.4% poor), negative attitude (79.3%) and poor practice (43.8%) of safety protocols regarding COVID-19. Conclusions: The findings of our study suggest that rickshaw pullers have poor level of knowledge regarding COVID-19, and their attitude towards it is negative, and also they practice COVID-19 safety protocols poorly. Northern International Medical College Journal Vol. 14 No. 1-2 July 2022-January 2023, Page 628-632
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".