Knowledge, Attitudes and Practices towards COVID-19 and its vaccine Among Palestinian population: Cross-sectional study
Bibliographic record
Abstract
Introduction: A COVID-19 pandemic was declared in March of 2020. Until the 12th of February 2022, there have been 556,550 confirmed cases and 5,128 deaths. Objectives: This study was done to assess Palestinian population's knowledge, practice, and attitude (KAP) toward the COVID-19 pandemic and its vaccine. Methodology: A cross-sectional survey of adult Palestinians above 18 years of age. A total of 1030 surveys were collected in the last quarter of 2021. Statistical Package for Social Sciences (SPSS) was used to analyze the data. Results: 34.5% of the participants were males, and 65.5% were females. 59.8% reported that they are committed to all the precautionary measures. 71.8% of participants have taken the vaccine or they will take it at the earliest opportunity. 73.9% of participants believed that commitment to physical distancing is one of the most important means to stop the pandemic, and 72% believed that wearing masks in public places is one of the most important means to stop the pandemic. Discussion: A higher vaccination acceptance rate was significantly higher among those with higher educational levels, working in the medical field, and participants following approved governmental media sources rather than social media. Conclusion: Adherence of Palestinian population towards taking vaccines and commitment to protective measures was high. This has helped -despite the challenge of living under occupation- in minimizing the burden of the pandemic in our society. Keywords: COVID-19, COVID-19 vaccines, Knowledge, attitude, practice, KAP
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".