Exploring community perceptions, attitudes and practices regarding the COVID-19 pandemic in Karachi, Pakistan
Bibliographic record
Abstract
BACKGROUND: The Government of Pakistan is facing difficulty to contain the surge of COVID-19 due to the country's social, political, economical and cultural context. Experiences from the previous epidemic suggest that community perceptions, social norms and cultural practices can impede COVID-19 containment. To understand social responses towards COVID-19, the study aims to explore the understanding of COVID-19 and the acceptance of control measures among community members. METHODS: We conducted an exploratory qualitative study using a purposive sampling approach, at two communities of Karachi, Pakistan. In-depth interviews were conducted with community members including, young, middle-aged and older adults of both genders. Study data were analysed manually using the conventional content analysis technique. RESULTS: A total of 27 in-depth virtual interviews were conducted, between May and June 2020. Six overarching themes were identified: (1) community knowledge and perceptions around COVID-19; (2) trusted and preferred sources of health information; (3) initial thoughts and feeling towards COVID-19 pandemic; (4) community practices to prevent exposure from COVID-19; (5) perceived risks associated with poor adherence to infection control practices; and (6) future preparedness of community to avoid the second wave of the outbreak. Generally, community members had good knowledge about COVID-19, and positive behaviour and attitude towards using standard precautions. The knowledge is mainly acquired through electronic, print and social media platforms, which have pros and cons. However, some community members including younger individuals had poor adherence to safety measures. This may necessitate concentrated efforts to raise awareness through community mobilisation and sensitisation activities. CONCLUSION: This study provides an initial evidence base of communities' perceptions, and attitudes towards COVID-19 in an early stage of pandemic. The study emphasises that sufficient knowledge and awareness about COVID-19, adequate training and drills, and adherence to safety measures, are necessary to better prepare for the second wave of COVID-19.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".