Understanding of adolescents’ knowledge, attitudes, and prevention practices toward COVID-19 using a web-based cross-sectional study
Bibliographic record
Abstract
In Pakistan effective border control measures and school closures were implemented after declaration of COVID-19 pandemic. Public awareness campaigns were started to educate public including adolescents. This study aims to assess adolescent's knowledge, attitudes and preventive practices during pandemic in Pakistan. A cross-sectional study was conducted among 328 individuals from October 2021 to February 2022 among school going adolescents aged 10-19 years in Pakistan. An online questionnaire was administered using online platforms. The questionnaire included sections on socio-demographic information, knowledge, attitudes, preventive practices, vaccine practice and information on new COVID-19 variants. Data was analyzed using multiple linear regression method. Among these individuals, mean knowledge scores were 9.64 (3.53), mean attitude score were 4.12 (1.77) and mean preventive practice score were 17 (6.83). Older adolescents exhibited better adherence to preventive measures and support to travel bans and adherence to SOPs during lock down. Multiple linear regression revealed that higher knowledge (β = 0.22, p = 0.03) and positive attitudes (β = 0.92, p < 0.001) were significantly associated with better preventive practices. This indicated that adolescents living in Pakistan have moderate knowledge about COVID-19 pandemic, with positive attitudes towards preventive measures taken by the government. However, there is a need for targeted educational interventions to enhance understanding and adherence to preventive measures among adolescents to better manage the pandemic in near future.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".