Knowledge, attitudes, practice of people toward the COVID-19 pandemics, and its impact in Afghanistan
Bibliographic record
Abstract
COVID-19 pandemic disrupted the social and economic norms. Knowledge, Attitude and Practices studies are used to address the information gap for further strategic decision making to control the pandemic. This study aimed to find the level of Knowledge, Awareness, Attitudes, and behavioral practices of the people of Afghanistan about the COVID-19 and its impact on health and socio-economic dimension of their routine lives. We used a cross-sectional method with two stage sampling design. Data analysis was performed using SPSS v.20. The survey focused on adults including men and women all over the country to represent the country, including the urban and rural areas. Most of the respondents are in the age group of 21-30 years (46.5%); 60.15% of the participants are married. Almost 75% of females and 84% of males were literate and most participants have a bachelor's degree (34%). More than 80% of participants knew they can prevent themselves from contacting COVID-19 through hand washing frequently with soap and water and wearing a mask. More than 80% of participants responded that they would go for a lab test for detection of the virus as well as COVID-19 vaccination if it is available. Almost 35% reported always wearing a mask to prevent COVID-19 transmission; more than half of participants always wash their hands, more than 60% of them do not touch their eyes, nose, and mouth frequently. Nearly 60% of participants indicated that their household had problems satisfying food needs partly during the COVID-19 pandemic. Nearly half of participants believed that the government was successful in applying lockdown measures and in awareness rising (56.8 and 69.8%). The study findings provide some useful insight about the KAP of communities in Afghanistan, which could assist policy makers in public health to design and implement interventions based on the information gaps reported.
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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 | 0.003 |
| 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 | 0.000 |
| 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".